From b33e91dfb83be9d55fb285be968af38951611579 Mon Sep 17 00:00:00 2001 From: chrishayuk Date: Sun, 1 Feb 2026 01:16:39 +0000 Subject: [PATCH 1/5] added experiment changes --- .../minimum_viable_6layer.py | 392 ++++++++++++++ .../minimum_viable_routing.py | 492 ++++++++++++++++++ .../writeup/00-executive-summary.md | 1 + .../writeup/23-appendix.md | 1 + .../writeup/24-minimum-viable-routing.md | 130 +++++ .../memory_fact_retrieval/writeup/README.md | 1 + 6 files changed, 1017 insertions(+) create mode 100644 experiments/expert_function_classification/minimum_viable_6layer.py create mode 100644 experiments/expert_function_classification/minimum_viable_routing.py create mode 100644 experiments/memory_fact_retrieval/writeup/24-minimum-viable-routing.md diff --git a/experiments/expert_function_classification/minimum_viable_6layer.py b/experiments/expert_function_classification/minimum_viable_6layer.py new file mode 100644 index 00000000..5e106885 --- /dev/null +++ b/experiments/expert_function_classification/minimum_viable_6layer.py @@ -0,0 +1,392 @@ +#!/usr/bin/env python3 +"""Quick 6-layer test to pin the cliff between 5 and 7 layers. + +7 layers [0,3,7,11,15,19,23]: 8/8 MB +5 layers [0,5,11,17,23]: 4/8 MB + +Test 6 layers to find the exact boundary. + +Run: python experiments/expert_function_classification/minimum_viable_6layer.py +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + +import mlx.core as mx +import numpy as np + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + +FACTS = [ + { + "prompt": "What is the capital of France?", + "bare": "The capital of France is", + "expected": "Paris", + "mb_entry": "France | capital | Paris", + }, + { + "prompt": "What is the chemical symbol for gold?", + "bare": "The chemical symbol for gold is", + "expected": "Au", + "mb_entry": "Gold | chemical symbol | Au", + }, + { + "prompt": "Who wrote Romeo and Juliet?", + "bare": "The author of Romeo and Juliet is", + "expected": "Shakespeare", + "mb_entry": "Romeo and Juliet | author | William Shakespeare", + }, + { + "prompt": "What is the speed of light in m/s?", + "bare": "The speed of light is approximately", + "expected": "299", + "mb_entry": "Speed of light | value | 299,792,458 meters per second", + }, + { + "prompt": "Who is the CEO of Microsoft?", + "bare": "The CEO of Microsoft is", + "expected": "Nadella", + "mb_entry": "Microsoft | CEO | Satya Nadella", + }, + { + "prompt": "What is the capital of Japan?", + "bare": "The capital of Japan is", + "expected": "Tokyo", + "mb_entry": "Japan | capital | Tokyo", + }, + { + "prompt": "What is the chemical symbol for silver?", + "bare": "The chemical symbol for silver is", + "expected": "Ag", + "mb_entry": "Silver | chemical symbol | Ag", + }, + { + "prompt": "What is the capital of Australia?", + "bare": "The capital of Australia is", + "expected": "Canberra", + "mb_entry": "Australia | capital | Canberra", + }, +] + +COHERENCE_PROMPTS = [ + "Once upon a time there was a", + "The process of photosynthesis involves", +] + +MAX_TOKENS = 40 +FIXED_EXPERTS = [0, 8, 16, 24] + +# 6-layer configs to test: two spacing strategies +CONDITIONS: dict[str, list[int]] = { + "L0_plus_6_even": [0, 5, 9, 14, 18, 23], # 6 layers, ~gap 4.6 (evenly spaced) + "L0_plus_6_tight": [0, 3, 7, 11, 15, 19], # 6 layers, gap 3-4 (tighter, front-loaded) +} + + +def compute_repetition_ratio(text: str, n: int = 3) -> float: + words = text.split() + if len(words) < n: + return 0.0 + ngrams = [tuple(words[i : i + n]) for i in range(len(words) - n + 1)] + if not ngrams: + return 0.0 + return 1.0 - len(set(ngrams)) / len(ngrams) + + +def build_memory_bank_prompt(question: str, all_entries: list[str]) -> str: + mb_block = "\n".join(f"- {entry}" for entry in all_entries) + return ( + f"[Memory Bank]\n{mb_block}\n[End Memory Bank]\n\n" + f"Using the memory bank above, answer: {question}\nAnswer:" + ) + + +class SixLayerTest: + def __init__(self): + self.model = None + self.tokenizer = None + self.results: list[dict] = [] + self._router_class = None + self._original_router_call = None + + async def setup(self): + from chuk_lazarus.introspection.moe.expert_router import ExpertRouter + + logger.info("Loading model...") + router = await ExpertRouter.from_pretrained("openai/gpt-oss-20b") + self.model = router._model + self.tokenizer = router._tokenizer + + sample_layer = self.model.model.layers[0] + self._router_class = type(sample_layer.mlp.router) + self._original_router_call = self._router_class.__call__ + logger.info(" Ready.") + + def _generate(self, prompt: str, max_tokens: int = MAX_TOKENS) -> str: + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + generated: list[int] = [] + cache = None + + for _ in range(max_tokens): + output = self.model(input_ids, cache=cache) + if hasattr(output, "logits"): + logits = output.logits + cache = getattr(output, "cache", None) + elif isinstance(output, tuple): + logits, cache = output + else: + logits = output + cache = None + + next_token = int(mx.argmax(logits[:, -1, :], axis=-1).item()) + generated.append(next_token) + if next_token == self.tokenizer.eos_token_id: + break + input_ids = mx.array([[next_token]]) + + return self.tokenizer.decode(generated).strip() + + def _generate_with_partial_routing( + self, prompt: str, learned_layers: set[int] + ) -> str: + experiment = self + original_call = self._original_router_call + + def patched_router(router_self: Any, x: mx.array) -> tuple[mx.array, mx.array]: + layer_idx = -1 + for i, layer in enumerate(experiment.model.model.layers): + if hasattr(layer, "mlp") and hasattr(layer.mlp, "router"): + if layer.mlp.router is router_self: + layer_idx = i + break + + if layer_idx in learned_layers: + return original_call(router_self, x) + + if x.ndim == 3: + x_flat = x.reshape(-1, x.shape[-1]) + else: + x_flat = x + + num_tokens = x_flat.shape[0] + k = router_self.num_experts_per_tok + fixed = FIXED_EXPERTS[:k] + indices = np.tile(np.array(fixed, dtype=np.int32), (num_tokens, 1)) + indices_mx = mx.array(indices) + weights_mx = mx.ones((num_tokens, k)) / k + return weights_mx, indices_mx + + try: + self._router_class.__call__ = patched_router + result = self._generate(prompt) + finally: + self._router_class.__call__ = self._original_router_call + + return result + + async def run_condition(self, name: str, learned_layers: list[int]): + n_learned = len(learned_layers) + n_fixed = 24 - n_learned + logger.info(f"\n {name}: {n_learned} learned, {n_fixed} fixed") + + loop = asyncio.get_event_loop() + learned_set = set(learned_layers) + all_mb_entries = [f["mb_entry"] for f in FACTS] + + # Bare + facts_ok = 0 + total_rep = 0.0 + for fact in FACTS: + text = await loop.run_in_executor( + None, self._generate_with_partial_routing, + fact["bare"], learned_set, + ) + mx.eval(mx.zeros(1)) + preserved = fact["expected"].lower() in text.lower() + rep = compute_repetition_ratio(text) + if preserved: + facts_ok += 1 + total_rep += rep + self.results.append({ + "condition": name, "use_mb": False, + "n_learned": n_learned, "learned_layers": learned_layers, + "prompt": fact["bare"], "expected": fact["expected"], + "text": text, "fact_preserved": preserved, + "repetition_ratio": rep, + }) + + avg_rep = total_rep / len(FACTS) + logger.info(f" Bare: {facts_ok}/8 facts, avg_rep={avg_rep:.3f}") + + # MB + mb_facts_ok = 0 + mb_total_rep = 0.0 + for fact in FACTS: + mb_prompt = build_memory_bank_prompt(fact["prompt"], all_mb_entries) + text = await loop.run_in_executor( + None, self._generate_with_partial_routing, + mb_prompt, learned_set, + ) + mx.eval(mx.zeros(1)) + preserved = fact["expected"].lower() in text.lower() + rep = compute_repetition_ratio(text) + if preserved: + mb_facts_ok += 1 + mb_total_rep += rep + self.results.append({ + "condition": name, "use_mb": True, + "n_learned": n_learned, "learned_layers": learned_layers, + "prompt": fact["prompt"], "expected": fact["expected"], + "text": text, "fact_preserved": preserved, + "repetition_ratio": rep, + }) + + mb_avg_rep = mb_total_rep / len(FACTS) + logger.info(f" MB: {mb_facts_ok}/8 facts, avg_rep={mb_avg_rep:.3f}") + + # Coherence + for prompt in COHERENCE_PROMPTS: + text = await loop.run_in_executor( + None, self._generate_with_partial_routing, + prompt, learned_set, + ) + mx.eval(mx.zeros(1)) + rep = compute_repetition_ratio(text) + self.results.append({ + "condition": name, "use_mb": False, + "n_learned": n_learned, "learned_layers": learned_layers, + "prompt": prompt, "expected": None, + "text": text, "fact_preserved": None, + "repetition_ratio": rep, + }) + + def _print_summary(self): + print("\n" + "=" * 90) + print("6-LAYER CLIFF TEST RESULTS") + print("=" * 90) + + by_cond: dict[str, list[dict]] = defaultdict(list) + for r in self.results: + by_cond[r["condition"]].append(r) + + print(f"\n{'Condition':>18} | {'Learned':>7} | {'Bare':>6} | {'MB':>6} | {'BareRep':>7} | {'MBRep':>7} | Layers") + print("-" * 105) + + for name in CONDITIONS: + results = by_cond.get(name, []) + if not results: + continue + bare_facts = [r for r in results if not r["use_mb"] and r["expected"] is not None] + mb_facts = [r for r in results if r["use_mb"] and r["expected"] is not None] + bare_ok = sum(1 for r in bare_facts if r["fact_preserved"]) + mb_ok = sum(1 for r in mb_facts if r["fact_preserved"]) + bare_rep = sum(r["repetition_ratio"] for r in bare_facts) / len(bare_facts) if bare_facts else 0 + mb_rep = sum(r["repetition_ratio"] for r in mb_facts) / len(mb_facts) if mb_facts else 0 + n_learned = results[0]["n_learned"] + layers_str = str(CONDITIONS[name]) + + print( + f"{name:>18} | {n_learned:>3}/24 | " + f"{bare_ok}/8 | {mb_ok}/8 | " + f"{bare_rep:>7.3f} | {mb_rep:>7.3f} | {layers_str}" + ) + + # Context from prior experiments + print("\n--- FULL SCALING CURVE (with prior results) ---") + print() + prior = [ + (0, "none", "0/8", "0/8"), + (1, "L0_only", "0/8", "0/8"), + (2, "L0_endpoints", "0/8", "0/8"), + (3, "L0_plus_mid", "0/8", "1/8"), + (5, "L0_plus_5", "0/8", "4/8"), + ] + print(" Prior results:") + for n, name, bare, mb in prior: + print(f" {n:>2} layers ({name:>18}): bare={bare}, MB={mb}") + + print(" New results:") + for name in CONDITIONS: + results = by_cond.get(name, []) + bare_facts = [r for r in results if not r["use_mb"] and r["expected"] is not None] + mb_facts = [r for r in results if r["use_mb"] and r["expected"] is not None] + bare_ok = sum(1 for r in bare_facts if r["fact_preserved"]) + mb_ok = sum(1 for r in mb_facts if r["fact_preserved"]) + n = len(CONDITIONS[name]) + print(f" {n:>2} layers ({name:>18}): bare={bare_ok}/8, MB={mb_ok}/8 *** NEW ***") + + print(" Prior results:") + print(f" {'7':>2} layers ({'L0_plus_7':>18}): bare=1/8, MB=8/8") + print(f" {'12':>2} layers ({'L0_only_extra':>18}): bare=6/8, MB=8/8 (Part 20)") + print(f" {'24':>2} layers ({'normal':>18}): bare=8/8, MB=8/8") + + # Sample outputs + print("\n--- Sample: 'The capital of France is' ---") + for name in CONDITIONS: + for r in self.results: + if r["condition"] == name and "France" in r["prompt"]: + text_short = r["text"][:60].replace("\n", " ") + status = "ok" if r["fact_preserved"] else "FAIL" + mode = "MB" if r["use_mb"] else "bare" + print(f" {name:>18} ({mode:>4}): [{status:>4}] {text_short}") + + def _save_results(self) -> Path: + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = results_dir / f"minimum_viable_6layer_{timestamp}.json" + + with open(output_path, "w") as f: + json.dump( + { + "metadata": { + "experiment": "minimum_viable_6layer", + "model": "openai/gpt-oss-20b", + "timestamp": timestamp, + "conditions": { + k: {"learned_layers": v, "n_learned": len(v)} + for k, v in CONDITIONS.items() + }, + }, + "results": self.results, + }, + f, + indent=2, + ) + logger.info(f"\nResults saved to {output_path}") + return output_path + + async def run(self): + await self.setup() + n_passes = len(CONDITIONS) * (len(FACTS) * 2 + len(COHERENCE_PROMPTS)) + logger.info("=" * 60) + logger.info("6-LAYER CLIFF TEST") + logger.info(f" Conditions: {len(CONDITIONS)}") + logger.info(f" Total passes: ~{n_passes}") + logger.info("=" * 60) + + for name, learned_layers in CONDITIONS.items(): + await self.run_condition(name, learned_layers) + + self._save_results() + self._print_summary() + + +async def main(): + experiment = SixLayerTest() + await experiment.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/experiments/expert_function_classification/minimum_viable_routing.py b/experiments/expert_function_classification/minimum_viable_routing.py new file mode 100644 index 00000000..f62fdfc6 --- /dev/null +++ b/experiments/expert_function_classification/minimum_viable_routing.py @@ -0,0 +1,492 @@ +#!/usr/bin/env python3 +"""Minimum Viable Routing Experiment. + +Part 20 showed L0 is the gatekeeper (~2 facts). The best 12-layer config +(L0_only_extra) achieved 6/8 bare, 8/8 with MB. + +Question: How few learned layers do we actually need? + +Conditions (all include L0, plus evenly spaced layers): + A. normal - all 24 learned (baseline) + B. none - 0 learned (floor) + C. L0_only - [0] alone (1 layer) + D. L0_endpoints - [0, 23] (2 layers) + E. L0_plus_mid - [0, 11, 23] (3 layers) + F. L0_plus_5 - [0, 5, 11, 17, 23] (5 layers) + G. L0_plus_7 - [0, 3, 7, 11, 15, 19, 23] (7 layers) + +Each tested bare and with memory bank. + +If L0 + MB alone gets 8/8, that's 1/24 learned routers for full accuracy. + +Run: python experiments/expert_function_classification/minimum_viable_routing.py +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + +import mlx.core as mx +import numpy as np + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + +FACTS = [ + { + "prompt": "What is the capital of France?", + "bare": "The capital of France is", + "expected": "Paris", + "mb_entry": "France | capital | Paris", + }, + { + "prompt": "What is the chemical symbol for gold?", + "bare": "The chemical symbol for gold is", + "expected": "Au", + "mb_entry": "Gold | chemical symbol | Au", + }, + { + "prompt": "Who wrote Romeo and Juliet?", + "bare": "The author of Romeo and Juliet is", + "expected": "Shakespeare", + "mb_entry": "Romeo and Juliet | author | William Shakespeare", + }, + { + "prompt": "What is the speed of light in m/s?", + "bare": "The speed of light is approximately", + "expected": "299", + "mb_entry": "Speed of light | value | 299,792,458 meters per second", + }, + { + "prompt": "Who is the CEO of Microsoft?", + "bare": "The CEO of Microsoft is", + "expected": "Nadella", + "mb_entry": "Microsoft | CEO | Satya Nadella", + }, + { + "prompt": "What is the capital of Japan?", + "bare": "The capital of Japan is", + "expected": "Tokyo", + "mb_entry": "Japan | capital | Tokyo", + }, + { + "prompt": "What is the chemical symbol for silver?", + "bare": "The chemical symbol for silver is", + "expected": "Ag", + "mb_entry": "Silver | chemical symbol | Ag", + }, + { + "prompt": "What is the capital of Australia?", + "bare": "The capital of Australia is", + "expected": "Canberra", + "mb_entry": "Australia | capital | Canberra", + }, +] + +COHERENCE_PROMPTS = [ + "Once upon a time there was a", + "The process of photosynthesis involves", +] + +MAX_TOKENS = 40 + +FIXED_EXPERTS = [0, 8, 16, 24] + +# Routing conditions: name -> list of layer indices that keep learned routing +# Ordered from most to fewest learned layers +CONDITIONS: dict[str, list[int]] = { + "normal": list(range(24)), # 24 layers (baseline) + "L0_plus_7": [0, 3, 7, 11, 15, 19, 23], # 7 layers + "L0_plus_5": [0, 5, 11, 17, 23], # 5 layers + "L0_plus_mid": [0, 11, 23], # 3 layers + "L0_endpoints": [0, 23], # 2 layers + "L0_only": [0], # 1 layer + "none": [], # 0 layers (floor) +} + + +def compute_repetition_ratio(text: str, n: int = 3) -> float: + words = text.split() + if len(words) < n: + return 0.0 + ngrams = [tuple(words[i : i + n]) for i in range(len(words) - n + 1)] + if not ngrams: + return 0.0 + return 1.0 - len(set(ngrams)) / len(ngrams) + + +def build_memory_bank_prompt(question: str, all_entries: list[str]) -> str: + mb_block = "\n".join(f"- {entry}" for entry in all_entries) + return ( + f"[Memory Bank]\n{mb_block}\n[End Memory Bank]\n\n" + f"Using the memory bank above, answer: {question}\nAnswer:" + ) + + +class MinimumViableRouting: + def __init__(self): + self.model = None + self.tokenizer = None + self.results: list[dict] = [] + self._router_class = None + self._original_router_call = None + + async def setup(self): + from chuk_lazarus.introspection.moe.expert_router import ExpertRouter + + logger.info("Loading model...") + router = await ExpertRouter.from_pretrained("openai/gpt-oss-20b") + self.model = router._model + self.tokenizer = router._tokenizer + + sample_layer = self.model.model.layers[0] + self._router_class = type(sample_layer.mlp.router) + self._original_router_call = self._router_class.__call__ + logger.info(" Ready.") + + def _generate(self, prompt: str, max_tokens: int = MAX_TOKENS) -> str: + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + generated: list[int] = [] + cache = None + + for _ in range(max_tokens): + output = self.model(input_ids, cache=cache) + if hasattr(output, "logits"): + logits = output.logits + cache = getattr(output, "cache", None) + elif isinstance(output, tuple): + logits, cache = output + else: + logits = output + cache = None + + next_token = int(mx.argmax(logits[:, -1, :], axis=-1).item()) + generated.append(next_token) + if next_token == self.tokenizer.eos_token_id: + break + input_ids = mx.array([[next_token]]) + + return self.tokenizer.decode(generated).strip() + + def _generate_with_partial_routing( + self, prompt: str, learned_layers: set[int] + ) -> str: + experiment = self + original_call = self._original_router_call + + def patched_router(router_self: Any, x: mx.array) -> tuple[mx.array, mx.array]: + layer_idx = -1 + for i, layer in enumerate(experiment.model.model.layers): + if hasattr(layer, "mlp") and hasattr(layer.mlp, "router"): + if layer.mlp.router is router_self: + layer_idx = i + break + + if layer_idx in learned_layers: + return original_call(router_self, x) + + if x.ndim == 3: + x_flat = x.reshape(-1, x.shape[-1]) + else: + x_flat = x + + num_tokens = x_flat.shape[0] + k = router_self.num_experts_per_tok + fixed = FIXED_EXPERTS[:k] + indices = np.tile(np.array(fixed, dtype=np.int32), (num_tokens, 1)) + indices_mx = mx.array(indices) + weights_mx = mx.ones((num_tokens, k)) / k + return weights_mx, indices_mx + + try: + self._router_class.__call__ = patched_router + result = self._generate(prompt) + finally: + self._router_class.__call__ = self._original_router_call + + return result + + async def run_condition(self, name: str, learned_layers: list[int]): + n_learned = len(learned_layers) + n_fixed = 24 - n_learned + logger.info(f"\n {name}: {n_learned} learned, {n_fixed} fixed") + + loop = asyncio.get_event_loop() + learned_set = set(learned_layers) + all_mb_entries = [f["mb_entry"] for f in FACTS] + + # Test bare prompts (no memory bank) + facts_ok = 0 + total_rep = 0.0 + + for fact in FACTS: + if name == "normal": + text = await loop.run_in_executor( + None, self._generate, fact["bare"] + ) + else: + text = await loop.run_in_executor( + None, self._generate_with_partial_routing, + fact["bare"], learned_set, + ) + mx.eval(mx.zeros(1)) + + preserved = fact["expected"].lower() in text.lower() + rep = compute_repetition_ratio(text) + if preserved: + facts_ok += 1 + total_rep += rep + + self.results.append({ + "condition": name, + "use_mb": False, + "n_learned": n_learned, + "learned_layers": learned_layers, + "prompt": fact["bare"], + "expected": fact["expected"], + "text": text, + "fact_preserved": preserved, + "repetition_ratio": rep, + }) + + avg_rep = total_rep / len(FACTS) + logger.info(f" Bare: {facts_ok}/8 facts, avg_rep={avg_rep:.3f}") + + # Test with memory bank + mb_facts_ok = 0 + mb_total_rep = 0.0 + + for fact in FACTS: + mb_prompt = build_memory_bank_prompt(fact["prompt"], all_mb_entries) + if name == "normal": + text = await loop.run_in_executor( + None, self._generate, mb_prompt + ) + else: + text = await loop.run_in_executor( + None, self._generate_with_partial_routing, + mb_prompt, learned_set, + ) + mx.eval(mx.zeros(1)) + + preserved = fact["expected"].lower() in text.lower() + rep = compute_repetition_ratio(text) + if preserved: + mb_facts_ok += 1 + mb_total_rep += rep + + self.results.append({ + "condition": name, + "use_mb": True, + "n_learned": n_learned, + "learned_layers": learned_layers, + "prompt": fact["prompt"], + "expected": fact["expected"], + "text": text, + "fact_preserved": preserved, + "repetition_ratio": rep, + }) + + mb_avg_rep = mb_total_rep / len(FACTS) + logger.info(f" MB: {mb_facts_ok}/8 facts, avg_rep={mb_avg_rep:.3f}") + + # Coherence (bare only) + for prompt in COHERENCE_PROMPTS: + if name == "normal": + text = await loop.run_in_executor(None, self._generate, prompt) + else: + text = await loop.run_in_executor( + None, self._generate_with_partial_routing, + prompt, learned_set, + ) + mx.eval(mx.zeros(1)) + rep = compute_repetition_ratio(text) + + self.results.append({ + "condition": name, + "use_mb": False, + "n_learned": n_learned, + "learned_layers": learned_layers, + "prompt": prompt, + "expected": None, + "text": text, + "fact_preserved": None, + "repetition_ratio": rep, + }) + + def _print_summary(self): + print("\n" + "=" * 90) + print("MINIMUM VIABLE ROUTING RESULTS") + print("=" * 90) + + by_cond: dict[str, list[dict]] = defaultdict(list) + for r in self.results: + by_cond[r["condition"]].append(r) + + print(f"\n{'Condition':>14} | {'Learned':>7} | {'Bare':>6} | {'MB':>6} | {'BareRep':>7} | {'MBRep':>7} | Layers") + print("-" * 100) + + for name in CONDITIONS: + results = by_cond.get(name, []) + if not results: + continue + + bare_facts = [r for r in results if not r["use_mb"] and r["expected"] is not None] + mb_facts = [r for r in results if r["use_mb"] and r["expected"] is not None] + + bare_ok = sum(1 for r in bare_facts if r["fact_preserved"]) + mb_ok = sum(1 for r in mb_facts if r["fact_preserved"]) + + bare_rep = sum(r["repetition_ratio"] for r in bare_facts) / len(bare_facts) if bare_facts else 0 + mb_rep = sum(r["repetition_ratio"] for r in mb_facts) / len(mb_facts) if mb_facts else 0 + + n_learned = results[0]["n_learned"] + layers_str = str(CONDITIONS[name]) if len(CONDITIONS[name]) <= 7 else str(CONDITIONS[name][:6]) + "..." + + print( + f"{name:>14} | {n_learned:>3}/24 | " + f"{bare_ok}/8 | {mb_ok}/8 | " + f"{bare_rep:>7.3f} | {mb_rep:>7.3f} | {layers_str}" + ) + + # Find the minimum viable config + print("\n--- MINIMUM VIABLE ROUTING ---") + print() + + # Bare minimum + bare_scores = {} + mb_scores = {} + for name in CONDITIONS: + results = by_cond.get(name, []) + bare_facts = [r for r in results if not r["use_mb"] and r["expected"] is not None] + mb_facts = [r for r in results if r["use_mb"] and r["expected"] is not None] + bare_scores[name] = sum(1 for r in bare_facts if r["fact_preserved"]) + mb_scores[name] = sum(1 for r in mb_facts if r["fact_preserved"]) + + print(" Bare (no MB):") + for name in CONDITIONS: + n = len(CONDITIONS[name]) + marker = " <-- minimum" if bare_scores[name] >= 8 and ( + name == "none" or all( + bare_scores[c] < 8 + for c in CONDITIONS + if len(CONDITIONS[c]) < n and c != "normal" + ) + ) else "" + print(f" {name:>14}: {bare_scores[name]}/8 ({n:>2} layers){marker}") + + print() + print(" With memory bank:") + for name in CONDITIONS: + n = len(CONDITIONS[name]) + marker = " <-- minimum" if mb_scores[name] >= 8 and ( + name == "none" or all( + mb_scores[c] < 8 + for c in CONDITIONS + if len(CONDITIONS[c]) < n and c != "normal" + ) + ) else "" + print(f" {name:>14}: {mb_scores[name]}/8 ({n:>2} layers){marker}") + + # L0 vs none comparison + print("\n--- L0 EFFECT (bare) ---") + l0_bare = bare_scores.get("L0_only", 0) + none_bare = bare_scores.get("none", 0) + print(f" none: {none_bare}/8 (0 learned layers)") + print(f" L0_only: {l0_bare}/8 (1 learned layer)") + delta = l0_bare - none_bare + if delta > 0: + print(f" -> L0 alone adds {delta} facts") + elif delta == 0: + print(f" -> L0 alone makes no difference bare") + else: + print(f" -> L0 alone is worse by {-delta} facts (unexpected)") + + # MB rescue comparison + print("\n--- MB RESCUE ---") + l0_mb = mb_scores.get("L0_only", 0) + none_mb = mb_scores.get("none", 0) + print(f" none + MB: {none_mb}/8") + print(f" L0_only + MB: {l0_mb}/8") + + # Scaling curve + print("\n--- SCALING: Learned layers vs facts ---") + ordered = sorted(CONDITIONS.items(), key=lambda x: len(x[1])) + for name, layers in ordered: + n = len(layers) + print(f" {n:>2} layers: bare={bare_scores[name]}/8, MB={mb_scores[name]}/8") + + # Sample outputs for France + print("\n--- Sample: 'The capital of France is' ---") + for name in CONDITIONS: + for r in self.results: + if r["condition"] == name and "France" in r["prompt"]: + text_short = r["text"][:60].replace("\n", " ") + status = "ok" if r["fact_preserved"] else "FAIL" + mode = "MB" if r["use_mb"] else "bare" + print(f" {name:>14} ({mode:>4}): [{status:>4}] {text_short}") + + def _save_results(self) -> Path: + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = results_dir / f"minimum_viable_routing_{timestamp}.json" + + with open(output_path, "w") as f: + json.dump( + { + "metadata": { + "experiment": "minimum_viable_routing", + "model": "openai/gpt-oss-20b", + "timestamp": timestamp, + "conditions": { + k: {"learned_layers": v, "n_learned": len(v)} + for k, v in CONDITIONS.items() + }, + "prior_results": { + "layer_parity_L0_only_extra": "6/8 bare, 8/8 MB (12 layers)", + "layer_parity_even_learned": "5/8 bare, 8/8 MB (12 layers)", + }, + }, + "results": self.results, + }, + f, + indent=2, + ) + + logger.info(f"\nResults saved to {output_path}") + return output_path + + async def run(self): + await self.setup() + + n_passes = len(CONDITIONS) * (len(FACTS) * 2 + len(COHERENCE_PROMPTS)) + logger.info("=" * 60) + logger.info("MINIMUM VIABLE ROUTING EXPERIMENT") + logger.info(f" Conditions: {len(CONDITIONS)}") + logger.info(f" Total passes: ~{n_passes}") + logger.info("=" * 60) + + for name, learned_layers in CONDITIONS.items(): + await self.run_condition(name, learned_layers) + + self._save_results() + self._print_summary() + + +async def main(): + experiment = MinimumViableRouting() + await experiment.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/experiments/memory_fact_retrieval/writeup/00-executive-summary.md b/experiments/memory_fact_retrieval/writeup/00-executive-summary.md index 5d4962ca..48fef271 100644 --- a/experiments/memory_fact_retrieval/writeup/00-executive-summary.md +++ b/experiments/memory_fact_retrieval/writeup/00-executive-summary.md @@ -24,5 +24,6 @@ This study investigates how large language models store, retrieve, and process f 12. **Memory bank fully rescues the lite model** - Alternating fixed routing + memory bank injection = 8/8 facts (100%), identical to the full model. External knowledge compensates for 50% routing simplification on factual workloads. Counterfactual override also preserved at 100%. This validates a hybrid architecture: attention handles fact retrieval, MoE handles output quality, external memory compensates for MoE degradation 13. **L0 is the gatekeeper** - Removing L0 from learned routing drops facts from 5/8 → 3/8. Adding L0 to any alternating set adds ~2 facts. Best 12-layer config: L0 + spaced odds = 6/8 bare, 8/8 with MB. Even vs odd parity is mostly an L0 effect, not a fundamental layer property 14. **Hard cliff at gap=3** - Routing correction every 2 layers: 5/8 facts. Every 3 layers: 1/8 (cliff). Every 4+: 0/8. Memory bank shifts the threshold: 8/8 at gap=2, 6/8 at gap=3-4, 4-5/8 at gap=6-8, 0/8 at gap>8. Below ~4 learned layers, the model can't even read the memory bank +15. **Minimum viable: 6 layers (25%) + MB = 8/8** - L0 alone does nothing (necessary but not sufficient). Scaling from 0→24 learned layers, the MB cliff is between 5 and 7 layers. With tight spacing (gap ≤ 4), 6 layers at [0,3,7,11,15,19] achieves full MB rescue. With wide spacing (gap ~4.6), 6 layers fails at 2/8 MB. The last 4 layers (20-23) don't need learned routing. Three regimes: full (12+ layers, bare works), MB-dependent (6-7 layers, MB rescues), broken (≤5 layers, model too degraded) --- diff --git a/experiments/memory_fact_retrieval/writeup/23-appendix.md b/experiments/memory_fact_retrieval/writeup/23-appendix.md index cddf9c60..d0a804b6 100644 --- a/experiments/memory_fact_retrieval/writeup/23-appendix.md +++ b/experiments/memory_fact_retrieval/writeup/23-appendix.md @@ -40,3 +40,4 @@ 20. `memory_bank_lite` - Memory bank + degraded routing (fixed_alt+MB: 8/8 facts, skip_alt+MB: 6/8, counterfactual 100%) 21. `layer_parity` - Even vs odd layers + L0 importance (L0 worth ~2 facts; best 12-layer: L0+spaced odds = 6/8; all configs 8/8 with MB) 22. `layer_spacing` - How sparse can routing be (cliff at gap=3: 5/8→1/8; MB rescue gradient: 8/8 at gap=2, 4/8 at gap=8, 0/8 at gap>8) +23. `minimum_viable_routing` - How few learned layers (L0 alone: 0/8; 7 layers: 8/8 MB; 6 tight: 8/8 MB; 6 wide: 2/8 MB; spacing > count) diff --git a/experiments/memory_fact_retrieval/writeup/24-minimum-viable-routing.md b/experiments/memory_fact_retrieval/writeup/24-minimum-viable-routing.md new file mode 100644 index 00000000..9f2c0745 --- /dev/null +++ b/experiments/memory_fact_retrieval/writeup/24-minimum-viable-routing.md @@ -0,0 +1,130 @@ +## Part 22: Minimum Viable Routing — How Few Layers Can We Learn? + +### 22.1 The Question + +Part 20 showed L0 is the gatekeeper (~2 facts). Part 21 showed a hard cliff at gap=3. The best 12-layer config (L0_only_extra) achieved 6/8 bare, 8/8 with MB. + +But how low can we go? If L0 is critical, can L0 alone + MB achieve full accuracy? If not, what's the absolute minimum number of learned layers for 8/8 with memory bank? + +### 22.2 Experiment 1: Scaling Down + +All conditions include L0 (except `none`), with remaining layers evenly spaced across the 24-layer model: + +| Condition | Learned Layers | Count | +|-----------|---------------|-------| +| normal | [0-23] | 24 | +| L0_plus_7 | [0, 3, 7, 11, 15, 19, 23] | 7 | +| L0_plus_5 | [0, 5, 11, 17, 23] | 5 | +| L0_plus_mid | [0, 11, 23] | 3 | +| L0_endpoints | [0, 23] | 2 | +| L0_only | [0] | 1 | +| none | [] | 0 | + +### 22.3 Results: The Scaling Curve + +| Condition | Learned | Bare | +MB | Bare Rep | MB Rep | +|-----------|---------|------|-----|---------|--------| +| normal | 24/24 | **8/8** | **8/8** | 0.360 | 0.000 | +| L0_plus_7 | 7/24 | 1/8 | **8/8** | 0.266 | 0.262 | +| L0_plus_5 | 5/24 | 0/8 | 4/8 | 0.316 | 0.473 | +| L0_plus_mid | 3/24 | 0/8 | 1/8 | 0.588 | 0.769 | +| L0_endpoints | 2/24 | 0/8 | 0/8 | 0.798 | 0.774 | +| L0_only | 1/24 | 0/8 | 0/8 | 0.732 | 0.656 | +| none | 0/24 | 0/8 | 0/8 | 0.843 | 0.655 | + +``` +Bare facts: + 24 layers | ######## 8/8 + 7 layers | #....... 1/8 + 5 layers | ........ 0/8 + 3 layers | ........ 0/8 + 2 layers | ........ 0/8 + 1 layer | ........ 0/8 + 0 layers | ........ 0/8 + +With memory bank: + 24 layers | ######## 8/8 + 7 layers | ######## 8/8 ← minimum for full MB rescue + 5 layers | ####.... 4/8 ← cliff + 3 layers | #....... 1/8 + 2 layers | ........ 0/8 + 1 layer | ........ 0/8 + 0 layers | ........ 0/8 +``` + +### 22.4 L0 Alone Does Nothing + +A critical revision of Part 20's "gatekeeper" finding: **L0 is necessary but not sufficient**. + +``` +none: 0/8 bare, 0/8 MB (0 learned) +L0_only: 0/8 bare, 0/8 MB (1 learned) +``` + +L0 alone provides zero benefit. Without downstream learned layers to propagate the routing signal, L0's correct initialization has nothing to initialize *for*. The gatekeeper needs a gate to keep. + +### 22.5 The MB Cliff: Between 5 and 7 Layers + +The sharp transition from 4/8 to 8/8 MB occurs between 5 and 7 learned layers. To pin the exact boundary, we tested two 6-layer configurations: + +### 22.6 Experiment 2: The 6-Layer Cliff Test + +| Condition | Layers | Avg Gap | Bare | +MB | Bare Rep | MB Rep | +|-----------|--------|---------|------|-----|---------|--------| +| L0_plus_6_even | [0, 5, 9, 14, 18, 23] | 4.6 | 0/8 | 2/8 | 0.751 | 0.346 | +| **L0_plus_6_tight** | **[0, 3, 7, 11, 15, 19]** | **3.4** | **2/8** | **8/8** | 0.441 | 0.467 | + +Same layer count. Wildly different results. + +**The tight config (gap 3-4) achieves full 8/8 MB rescue.** The even config (gap ~4.6) fails at 2/8 MB. This confirms that spacing, not count, is the binding constraint. + +### 22.7 The Tight Config vs L0_plus_7 + +The tight 6-layer config `[0, 3, 7, 11, 15, 19]` is literally `L0_plus_7` minus L23: + +| Config | Layers | Bare | +MB | +|--------|--------|------|-----| +| L0_plus_6_tight | [0, 3, 7, 11, 15, 19] | 2/8 | **8/8** | +| L0_plus_7 | [0, 3, 7, 11, 15, 19, 23] | 1/8 | **8/8** | + +L23 adds nothing. The last 4 layers (20-23) don't need learned routing. The critical coverage is L0 through L19 — the first 83% of the network. + +### 22.8 Complete Scaling Curve + +Combining all results from Parts 20-22: + +| Learned | Spacing | Bare | +MB | Status | +|---------|---------|------|-----|--------| +| 24 (100%) | gap 1 | 8/8 | 8/8 | Full model | +| 12 (50%) | gap 2 | 5-6/8 | 8/8 | Part 20 optimum (bare) | +| 7 (29%) | gap 3.4 | 1/8 | 8/8 | MB minimum (wide) | +| **6 (25%)** | **gap 3.4** | **2/8** | **8/8** | **MB minimum (tight)** | +| 6 (25%) | gap 4.6 | 0/8 | 2/8 | Fails — gaps too wide | +| 5 (21%) | gap 5.8 | 0/8 | 4/8 | MB partially fails | +| 3 (13%) | gap 11 | 0/8 | 1/8 | Broken | +| 1-2 | — | 0/8 | 0/8 | Model can't read MB | + +### 22.9 The Rule + +**For full MB rescue: learned layers at gap ≤ 4, covering L0 through at least L19.** + +The minimum viable configuration is **6 learned layers (25%)** at `[0, 3, 7, 11, 15, 19]`: +- Bare: 2/8 facts (degraded but coherent) +- With MB: **8/8 facts (100%)** +- 75% of routers replaced with fixed experts + +### 22.10 Interpretation + +Three regimes emerge: + +| Regime | Layers | Gap | Bare | +MB | What's happening | +|--------|--------|-----|------|-----|-----------------| +| **Full** | 12+ | ≤ 2 | 5-8/8 | 8/8 | Bare recall works; MB is insurance | +| **MB-dependent** | 6-7 | 3-4 | 1-2/8 | 8/8 | Model coherent enough to read MB | +| **Broken** | ≤ 5 | ≥ 5 | 0/8 | 0-4/8 | Model too degraded for MB to help | + +The binding constraint is not the number of learned layers — it's whether the remaining learned layers maintain enough coherence for the model to process the memory bank prompt. Below 6 layers (gap > 4), the model generates repetitive nonsense and can't parse `[Memory Bank]` tokens. + +**Revised architecture**: The optimal lite model needs only 6 of 24 routers (25%) with tight spacing. The remaining 18 layers use fixed expert routing. Combined with memory bank injection, this achieves 100% factual accuracy at 75% routing savings. + +--- diff --git a/experiments/memory_fact_retrieval/writeup/README.md b/experiments/memory_fact_retrieval/writeup/README.md index 9cd3e04e..42b85b6a 100644 --- a/experiments/memory_fact_retrieval/writeup/README.md +++ b/experiments/memory_fact_retrieval/writeup/README.md @@ -38,5 +38,6 @@ 19. [Memory Bank + Lite Model](20-memory-bank-lite-model.md) - The compression path, hybrid architecture 20. [Layer Parity](21-layer-parity.md) - L0 is the gatekeeper, even vs odd 21. [Layer Spacing](22-layer-spacing.md) - Hard cliff at gap=3, MB rescue gradient +22. [Minimum Viable Routing](24-minimum-viable-routing.md) - 6 layers (25%) + MB = 8/8 ### [Appendix: Experiment Configuration](23-appendix.md) From d81afbd3140ca781dc065577849cdba095ac9ac0 Mon Sep 17 00:00:00 2001 From: chrishayuk Date: Mon, 2 Feb 2026 00:30:41 +0000 Subject: [PATCH 2/5] updated experiments for memory banks --- .../attention_at_emergence.py | 573 +++++++ .../layer_skip_emergence.py | 483 ++++++ .../memory_bank_injection_point.py | 678 ++++++++ .../residual_fact_emergence.py | 603 +++++++ .../expert_function_classification/results.md | 612 +++++++ .../residual_stream_dynamics/__init__.py | 1 + .../residual_stream_dynamics/config.yaml | 191 +++ .../residual_stream_dynamics/experiment.py | 1498 +++++++++++++++++ .../residual_stream_dynamics/results.md | 341 ++++ 9 files changed, 4980 insertions(+) create mode 100644 experiments/expert_function_classification/attention_at_emergence.py create mode 100644 experiments/expert_function_classification/layer_skip_emergence.py create mode 100644 experiments/expert_function_classification/memory_bank_injection_point.py create mode 100644 experiments/expert_function_classification/residual_fact_emergence.py create mode 100644 experiments/expert_function_classification/results.md create mode 100644 experiments/residual_stream_dynamics/__init__.py create mode 100644 experiments/residual_stream_dynamics/config.yaml create mode 100644 experiments/residual_stream_dynamics/experiment.py create mode 100644 experiments/residual_stream_dynamics/results.md diff --git a/experiments/expert_function_classification/attention_at_emergence.py b/experiments/expert_function_classification/attention_at_emergence.py new file mode 100644 index 00000000..91b66bec --- /dev/null +++ b/experiments/expert_function_classification/attention_at_emergence.py @@ -0,0 +1,573 @@ +#!/usr/bin/env python3 +"""Attention Pattern at Emergence Layers. + +Residual fact emergence showed facts crystallize at L20-21, with first +signal at L15. Layer skip showed L20-21 are important but not irreplaceable. + +This experiment asks: what is attention DOING at the emergence layers? +Specifically, does the final token (prediction position) attend to the +entity token (France, gold, Microsoft) when facts first appear? + +If attention focuses on the entity at emergence, that's the mechanistic +link: attention performs the "lookup" and writes the fact to the residual +stream. Experts then refine but don't originate the factual content. + +Method: + For each fact prompt, we: + 1. Identify the entity token position (e.g., "France" in "The capital of France is") + 2. Monkey-patch GptOssAttention to capture attention weights from Q, K, V + 3. At each layer (L0-L23), extract the final token's attention over all positions + 4. Measure how much attention the entity token receives vs other tokens + 5. Compare entity attention at pre-emergence layers vs emergence layers + +Run: python experiments/expert_function_classification/attention_at_emergence.py +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + +import mlx.core as mx + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +FACTS = [ + { + "prompt": "The capital of France is", + "expected_keyword": "Paris", + "entity": "France", + }, + { + "prompt": "The chemical symbol for gold is", + "expected_keyword": "Au", + "entity": "gold", + }, + { + "prompt": "The author of Romeo and Juliet is", + "expected_keyword": "Shakespeare", + "entity": "Romeo", # Multi-token entity; track first token + }, + { + "prompt": "The CEO of Microsoft is", + "expected_keyword": "Nadella", + "entity": "Microsoft", + }, + { + "prompt": "The capital of Japan is", + "expected_keyword": "Tokyo", + "entity": "Japan", + }, + { + "prompt": "The chemical symbol for silver is", + "expected_keyword": "Ag", + "entity": "silver", + }, + { + "prompt": "The capital of Australia is", + "expected_keyword": "Canberra", + "entity": "Australia", + }, +] + + +class AttentionAtEmergence: + """Capture attention patterns at fact emergence layers.""" + + def __init__(self): + self.model = None + self.tokenizer = None + self._attn_class = None + self._original_attn_call = None + self._captured_weights: dict[int, mx.array] = {} # layer -> [batch, heads, q_len, kv_len] + + async def setup(self): + from chuk_lazarus.introspection.moe.expert_router import ExpertRouter + + logger.info("Loading model: openai/gpt-oss-20b") + router = await ExpertRouter.from_pretrained("openai/gpt-oss-20b") + self.model = router._model + self.tokenizer = router._tokenizer + + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + mx.eval(self.model.parameters()) + + # Get the attention class for monkey-patching + sample_layer = self.model.model.layers[0] + self._attn_class = type(sample_layer.self_attn) + self._original_attn_call = self._attn_class.__call__ + + self.num_layers = len(self.model.model.layers) + logger.info(f"Model loaded: {self.num_layers} layers. Ready.") + + def _find_entity_position(self, prompt: str, entity: str) -> int | None: + """Find the token position of the entity in the prompt. + + Returns the position of the first token of the entity string. + """ + prompt_ids = self.tokenizer.encode(prompt) + + # Encode the entity with and without space prefix + for candidate in [f" {entity}", entity]: + try: + entity_ids = self.tokenizer.encode(candidate, add_special_tokens=False) + except TypeError: + entity_ids = self.tokenizer.encode(candidate) + + if not entity_ids: + continue + + # Find the first entity token in the prompt tokens + first_entity_id = entity_ids[0] + for pos, tid in enumerate(prompt_ids): + if tid == first_entity_id: + return pos + + return None + + def _capture_attention_forward( + self, prompt: str + ) -> dict[int, mx.array]: + """Run forward pass capturing attention weights at all layers. + + Monkey-patches GptOssAttention to manually compute attention weights + from Q, K, V (since mx.fast.scaled_dot_product_attention is fused + and doesn't expose them). + + Returns: {layer_idx: attention_weights} where weights have shape + [num_kv_groups, q_len, kv_len] (averaged across heads within each + KV group for memory efficiency). + """ + captured: dict[int, mx.array] = {} + experiment = self + original_call = self._original_attn_call + + def patched_attn( + attn_self: Any, + x: mx.array, + mask: mx.array | str | None = None, + cache: tuple[mx.array, mx.array] | None = None, + ) -> tuple[mx.array, tuple[mx.array, mx.array] | None]: + batch, seq_len, _ = x.shape + + # Compute Q, K, V (same as original) + q = attn_self.q_proj(x) + k = attn_self.k_proj(x) + v = attn_self.v_proj(x) + + q = q.reshape(batch, seq_len, attn_self.num_heads, attn_self.head_dim) + k = k.reshape(batch, seq_len, attn_self.num_kv_heads, attn_self.head_dim) + v = v.reshape(batch, seq_len, attn_self.num_kv_heads, attn_self.head_dim) + + q = q.transpose(0, 2, 1, 3) # [B, H, S, D] + k = k.transpose(0, 2, 1, 3) # [B, KV, S, D] + v = v.transpose(0, 2, 1, 3) + + # Apply RoPE + if cache is not None: + q = attn_self.rope(q, offset=cache[0].shape[2]) + k = attn_self.rope(k, offset=cache[0].shape[2]) + else: + q = attn_self.rope(q) + k = attn_self.rope(k) + + # Update cache + if cache is not None: + k = mx.concatenate([cache[0], k], axis=2) + v = mx.concatenate([cache[1], v], axis=2) + new_cache = (k, v) + + # Compute attention weights manually for capture + # Expand KV heads for GQA: [B, H, S, D] @ [B, KV, D, S] with head grouping + num_groups = attn_self.num_heads // attn_self.num_kv_heads # 64/8 = 8 + k_expanded = mx.repeat(k, num_groups, axis=1) # [B, H, S, D] + + # Compute scores: [B, H, S_q, S_k] + scores = (q @ k_expanded.transpose(0, 1, 3, 2)) * attn_self.scale + + # Apply mask + if mask is not None and not isinstance(mask, str): + scores = scores + mask + + # Softmax to get weights + weights = mx.softmax(scores, axis=-1) # [B, H, S_q, S_k] + + # Store: average across heads within each KV group for compactness + # [B, H, S_q, S_k] -> [B, KV_groups, S_q, S_k] by reshaping and averaging + weights_grouped = weights.reshape( + batch, attn_self.num_kv_heads, num_groups, seq_len, -1 + ) + weights_avg = mx.mean(weights_grouped, axis=2) # [B, KV, S_q, S_k] + + # Store the captured weights + layer_idx = attn_self.layer_idx + captured[layer_idx] = mx.stop_gradient(weights_avg[0]) # Drop batch dim + + # Still use the fused kernel for the actual output (for correctness) + output = mx.fast.scaled_dot_product_attention( + q, new_cache[0], new_cache[1], + scale=attn_self.scale, + mask=mask, + sinks=attn_self.sinks, + ) + output = output.transpose(0, 2, 1, 3) + output = output.reshape(batch, seq_len, -1) + output = attn_self.o_proj(output) + + return output, new_cache + + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + + try: + self._attn_class.__call__ = patched_attn + self.model(input_ids) + mx.eval(list(captured.values())) + finally: + self._attn_class.__call__ = self._original_attn_call + + return captured + + async def analyze_fact(self, fact: dict) -> dict[str, Any]: + """Analyze attention patterns for one fact.""" + prompt = fact["prompt"] + entity = fact["entity"] + keyword = fact["expected_keyword"] + + logger.info(f"\n Fact: {prompt} (entity='{entity}')") + + # Find entity position + entity_pos = self._find_entity_position(prompt, entity) + prompt_tokens = self.tokenizer.encode(prompt) + seq_len = len(prompt_tokens) + last_pos = seq_len - 1 + + if entity_pos is None: + logger.warning(f" Could not find entity '{entity}' in tokens") + return {"prompt": prompt, "error": f"entity '{entity}' not found"} + + # Decode tokens for reference + token_strs = [self.tokenizer.decode([tid]) for tid in prompt_tokens] + logger.info( + f" Tokens: {token_strs}" + ) + logger.info( + f" Entity '{entity}' at position {entity_pos} " + f"(token: '{token_strs[entity_pos]}')" + ) + logger.info(f" Last token at position {last_pos} (prediction point)") + + # Capture attention weights at all layers + loop = asyncio.get_event_loop() + captured = await loop.run_in_executor( + None, self._capture_attention_forward, prompt, + ) + + # Extract: for each layer, how much does the last token attend to the entity? + layer_entity_attention = {} + layer_max_attention = {} + layer_attention_distribution = {} + + for layer_idx in sorted(captured.keys()): + weights = captured[layer_idx] # [KV_groups, S_q, S_k] + # Get final token's attention across all KV groups + # Average across KV groups for a single attention score per position + final_token_attn = mx.mean(weights[:, last_pos, :], axis=0) # [S_k] + final_token_attn_list = final_token_attn.tolist() + + entity_attn = float(final_token_attn[entity_pos]) + max_attn = float(mx.max(final_token_attn)) + max_pos = int(mx.argmax(final_token_attn)) + + layer_entity_attention[layer_idx] = entity_attn + layer_max_attention[layer_idx] = { + "value": max_attn, + "position": max_pos, + "token": token_strs[max_pos] if max_pos < len(token_strs) else "?", + } + layer_attention_distribution[layer_idx] = { + str(pos): round(float(final_token_attn[pos]), 6) + for pos in range(seq_len) + } + + # Log key layers + for layer_idx in [0, 8, 15, 19, 20, 21, 22, 23]: + if layer_idx in layer_entity_attention: + ea = layer_entity_attention[layer_idx] + ma = layer_max_attention[layer_idx] + logger.info( + f" L{layer_idx:>2}: entity_attn={ea:.4f}, " + f"max={ma['value']:.4f} at pos {ma['position']} " + f"('{ma['token']}')" + ) + + return { + "prompt": prompt, + "entity": entity, + "entity_position": entity_pos, + "entity_token": token_strs[entity_pos], + "expected_keyword": keyword, + "seq_len": seq_len, + "tokens": token_strs, + "entity_attention_by_layer": { + str(k): round(v, 6) for k, v in layer_entity_attention.items() + }, + "max_attention_by_layer": { + str(k): v for k, v in layer_max_attention.items() + }, + "full_distribution_by_layer": { + str(k): v for k, v in layer_attention_distribution.items() + }, + } + + def _compute_summary(self, fact_results: list[dict]) -> dict[str, Any]: + """Compute aggregate attention statistics.""" + valid = [r for r in fact_results if "error" not in r] + + # Average entity attention by layer + avg_entity_attn: dict[int, list[float]] = defaultdict(list) + for r in valid: + for layer_str, attn in r["entity_attention_by_layer"].items(): + avg_entity_attn[int(layer_str)].append(attn) + + avg_curve = { + layer: round(sum(vals) / len(vals), 6) + for layer, vals in sorted(avg_entity_attn.items()) + } + + # Find peak entity attention layer + if avg_curve: + peak_layer = max(avg_curve, key=avg_curve.get) + peak_value = avg_curve[peak_layer] + else: + peak_layer = None + peak_value = None + + # Pre-emergence vs emergence vs post-emergence averages + pre = [avg_curve.get(l, 0) for l in range(0, 15)] + emergence = [avg_curve.get(l, 0) for l in range(15, 22)] + post = [avg_curve.get(l, 0) for l in range(22, 24)] + + avg_pre = sum(pre) / len(pre) if pre else 0 + avg_emergence = sum(emergence) / len(emergence) if emergence else 0 + avg_post = sum(post) / len(post) if post else 0 + + return { + "num_facts": len(valid), + "avg_entity_attention_by_layer": avg_curve, + "peak_entity_attention": { + "layer": peak_layer, + "value": peak_value, + }, + "phase_averages": { + "pre_emergence_L0_L14": round(avg_pre, 6), + "emergence_L15_L21": round(avg_emergence, 6), + "post_emergence_L22_L23": round(avg_post, 6), + }, + "interpretation": ( + f"Entity attention peaks at L{peak_layer} ({peak_value:.4f}). " + f"Pre-emergence avg: {avg_pre:.4f}, " + f"emergence avg: {avg_emergence:.4f}, " + f"post-emergence avg: {avg_post:.4f}." + ), + } + + def _print_summary(self, summary: dict, fact_results: list[dict]): + valid = [r for r in fact_results if "error" not in r] + + print("\n" + "=" * 90) + print("ATTENTION PATTERN AT EMERGENCE LAYERS - RESULTS") + print("=" * 90) + + # Per-fact entity attention at key layers + print( + f"\n{'Prompt':<36} | {'Entity':<12} | " + f"{'L0':>6} | {'L8':>6} | {'L15':>6} | {'L19':>6} | " + f"{'L20':>6} | {'L21':>6} | {'L22':>6} | {'L23':>6}" + ) + print("-" * 130) + + for r in valid: + prompt_short = r["prompt"][:34] + entity = r["entity"][:10] + attn = r["entity_attention_by_layer"] + cols = [] + for l in ["0", "8", "15", "19", "20", "21", "22", "23"]: + val = attn.get(l, 0) + cols.append(f"{val:.4f}") + print( + f"{prompt_short:<36} | {entity:<12} | " + + " | ".join(f"{c:>6}" for c in cols) + ) + + # Average entity attention curve + print("\n" + "-" * 90) + print("AVERAGE ENTITY ATTENTION BY LAYER (final token -> entity token)") + print("-" * 90) + + curve = summary["avg_entity_attention_by_layer"] + max_val = max(curve.values()) if curve else 1 + for layer in sorted(curve.keys()): + val = curve[layer] + bar_len = int((val / max(max_val, 0.001)) * 40) + bar = "#" * bar_len + marker = "" + if layer == 15: + marker = " <- first fact signal" + elif layer in (20, 21): + marker = " <- fact emergence" + print(f" L{layer:>2}: {val:>8.5f} |{bar}{marker}") + + # Phase comparison + print("\n" + "-" * 90) + print("PHASE AVERAGES") + print("-" * 90) + phases = summary["phase_averages"] + print(f" Pre-emergence (L0-L14): {phases['pre_emergence_L0_L14']:.5f}") + print(f" Emergence (L15-L21): {phases['emergence_L15_L21']:.5f}") + print(f" Post-emergence (L22-L23): {phases['post_emergence_L22_L23']:.5f}") + + ratio = ( + phases["emergence_L15_L21"] / phases["pre_emergence_L0_L14"] + if phases["pre_emergence_L0_L14"] > 0 else float("inf") + ) + print(f"\n Emergence/Pre-emergence ratio: {ratio:.1f}x") + + # Where does the last token attend most? (not just entity) + print("\n" + "-" * 90) + print("MOST-ATTENDED TOKEN BY LAYER (per fact)") + print("-" * 90) + + for r in valid: + prompt_short = r["prompt"][:35] + entity_pos = r["entity_position"] + print(f"\n {prompt_short} (entity at pos {entity_pos}):") + for l in ["15", "19", "20", "21", "22", "23"]: + ma = r["max_attention_by_layer"].get(l) + if ma: + is_entity = " <- ENTITY" if ma["position"] == entity_pos else "" + print( + f" L{l:>2}: max={ma['value']:.4f} at pos {ma['position']} " + f"('{ma['token']}'){is_entity}" + ) + + # Key findings + print("\n" + "=" * 90) + print("KEY FINDINGS") + print("=" * 90) + + peak = summary["peak_entity_attention"] + print(f"\n Entity attention peaks at: L{peak['layer']} ({peak['value']:.4f})") + + if ratio > 2.0: + print( + f"\n Attention to entity INCREASES {ratio:.1f}x at emergence layers." + ) + print( + " This confirms: attention performs the fact 'lookup' by focusing" + ) + print( + " on the entity token at exactly the layers where facts crystallize" + ) + print( + " in the residual stream." + ) + elif ratio > 1.0: + print( + f"\n Modest increase ({ratio:.1f}x) in entity attention at emergence." + ) + print( + " Fact crystallization may involve distributed attention rather than" + ) + print( + " a focused entity lookup." + ) + else: + print( + "\n Entity attention does NOT increase at emergence layers." + ) + print( + " Fact crystallization uses a different mechanism than direct" + ) + print( + " entity-token attention." + ) + + print("=" * 90) + + def _save_results(self, results: dict) -> Path: + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = results_dir / f"attention_at_emergence_{timestamp}.json" + + with open(output_path, "w") as f: + json.dump(results, f, indent=2, default=str) + logger.info(f"\nResults saved to {output_path}") + return output_path + + async def run(self): + await self.setup() + + logger.info("=" * 70) + logger.info("ATTENTION PATTERN AT EMERGENCE LAYERS") + logger.info(f" Facts: {len(FACTS)}") + logger.info("=" * 70) + + fact_results = [] + for fact in FACTS: + result = await self.analyze_fact(fact) + fact_results.append(result) + + summary = self._compute_summary(fact_results) + + output = { + "metadata": { + "experiment": "attention_at_emergence", + "model": "openai/gpt-oss-20b", + "timestamp": datetime.now().isoformat(), + "num_facts": len(FACTS), + "description": ( + "Captures attention weights at all layers to measure how " + "much the final (prediction) token attends to the entity " + "token at emergence layers (L15-L21). Tests whether " + "attention performs the fact 'lookup'." + ), + "method": ( + "Monkey-patches GptOssAttention to compute attention weights " + "from Q, K, V before the fused SDPA kernel. Averages across " + "heads within each KV group (8 KV groups, 8 heads per group). " + "Reports the mean across all KV groups." + ), + "prior_results": { + "fact_emergence_avg_top1": "L20.8 (logit lens)", + "fact_emergence_first_signal": "L15 (~5%)", + "layer_skip_L20_L21": "5/7 facts survive", + }, + }, + "summary": summary, + "fact_results": fact_results, + } + + self._save_results(output) + self._print_summary(summary, fact_results) + + +async def main(): + experiment = AttentionAtEmergence() + await experiment.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/experiments/expert_function_classification/layer_skip_emergence.py b/experiments/expert_function_classification/layer_skip_emergence.py new file mode 100644 index 00000000..53867759 --- /dev/null +++ b/experiments/expert_function_classification/layer_skip_emergence.py @@ -0,0 +1,483 @@ +#!/usr/bin/env python3 +"""Layer Skip at Emergence Point Experiment. + +Residual fact emergence showed facts crystallize at L20-21 (top-1 avg L20.8). +Knowledge ablation showed 0/8 facts break under expert ablation at any layer. + +This experiment tests: are the emergence layers (L20-21) *necessary* for +fact crystallization, or can the computation be deferred to later layers? + +We skip the MoE FFN entirely at specific layers (attention still runs, +residual passes through, but expert computation is zeroed). Then we: + 1. Generate text and check if the fact is preserved + 2. Run logit lens to see if/where the fact emerges under each condition + +Conditions: + A. normal - no skip (baseline) + B. skip_L20 - skip MoE at L20 only + C. skip_L21 - skip MoE at L21 only + D. skip_L20_L21 - skip MoE at L20 and L21 + E. skip_L19_L20_L21 - skip the full emergence window + F. skip_L15 - skip where first signal appears (control) + G. skip_L22_L23 - skip post-emergence layers (control) + +If skipping L20-21 kills facts: those layers are the bottleneck. +If facts still emerge at L22-23: the residual stream can crystallize later. + +Run: python experiments/expert_function_classification/layer_skip_emergence.py +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + +import mlx.core as mx + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +FACTS = [ + {"prompt": "The capital of France is", "expected_keyword": "Paris"}, + {"prompt": "The chemical symbol for gold is", "expected_keyword": "Au"}, + {"prompt": "The author of Romeo and Juliet is", "expected_keyword": "Shakespeare"}, + {"prompt": "The CEO of Microsoft is", "expected_keyword": "Nadella"}, + {"prompt": "The capital of Japan is", "expected_keyword": "Tokyo"}, + {"prompt": "The chemical symbol for silver is", "expected_keyword": "Ag"}, + {"prompt": "The capital of Australia is", "expected_keyword": "Canberra"}, +] + +# Skip conditions: name -> list of layer indices where MoE FFN is zeroed +CONDITIONS: dict[str, list[int]] = { + "normal": [], + "skip_L20": [20], + "skip_L21": [21], + "skip_L20_L21": [20, 21], + "skip_L19_L20_L21": [19, 20, 21], + "skip_L15": [15], + "skip_L22_L23": [22, 23], +} + +MAX_TOKENS = 40 + + +def compute_repetition_ratio(text: str, n: int = 3) -> float: + words = text.split() + if len(words) < n: + return 0.0 + ngrams = [tuple(words[i : i + n]) for i in range(len(words) - n + 1)] + if not ngrams: + return 0.0 + return 1.0 - len(set(ngrams)) / len(ngrams) + + +class LayerSkipEmergence: + """Test whether emergence layers are necessary for fact crystallization.""" + + def __init__(self): + self.model = None + self.tokenizer = None + self._mlp_class = None + self._original_mlp_call = None + self.results: list[dict] = [] + + async def setup(self): + from chuk_lazarus.introspection.moe.expert_router import ExpertRouter + + logger.info("Loading model: openai/gpt-oss-20b") + router = await ExpertRouter.from_pretrained("openai/gpt-oss-20b") + self.model = router._model + self.tokenizer = router._tokenizer + + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + mx.eval(self.model.parameters()) + + # Capture the MLP class for monkey-patching + sample_layer = self.model.model.layers[0] + self._mlp_class = type(sample_layer.mlp) + self._original_mlp_call = self._mlp_class.__call__ + + self.num_layers = len(self.model.model.layers) + logger.info(f"Model loaded: {self.num_layers} layers. Ready.") + + def _generate(self, prompt: str, max_tokens: int = MAX_TOKENS) -> str: + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + generated: list[int] = [] + cache = None + + for _ in range(max_tokens): + output = self.model(input_ids, cache=cache) + if hasattr(output, "logits"): + logits = output.logits + cache = getattr(output, "cache", None) + elif isinstance(output, tuple): + logits, cache = output + else: + logits = output + cache = None + + next_token = int(mx.argmax(logits[:, -1, :], axis=-1).item()) + generated.append(next_token) + if next_token == self.tokenizer.eos_token_id: + break + input_ids = mx.array([[next_token]]) + + return self.tokenizer.decode(generated).strip() + + def _generate_with_skip(self, prompt: str, skip_layers: set[int]) -> str: + """Generate text with MoE FFN zeroed at specified layers. + + Attention still runs. The residual stream passes through unchanged + at skipped layers (x = x + 0 = x). + """ + experiment = self + original_call = self._original_mlp_call + + def patched_mlp(mlp_self: Any, x: mx.array) -> mx.array: + # Find which layer this MLP belongs to + layer_idx = -1 + for i, layer in enumerate(experiment.model.model.layers): + if hasattr(layer, "mlp") and layer.mlp is mlp_self: + layer_idx = i + break + + if layer_idx in skip_layers: + # Return zeros: residual + 0 = residual (MoE skipped) + return mx.zeros_like(x) + + return original_call(mlp_self, x) + + try: + self._mlp_class.__call__ = patched_mlp + result = self._generate(prompt) + finally: + self._mlp_class.__call__ = self._original_mlp_call + + return result + + def _discover_fact_token(self, prompt: str) -> tuple[int, str]: + """Get the model's actual predicted next token for this prompt.""" + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + output = self.model(input_ids) + logits = output.logits if hasattr(output, "logits") else output + next_token_id = int(mx.argmax(logits[0, -1, :]).item()) + decoded = self.tokenizer.decode([next_token_id]) + return next_token_id, decoded + + def _run_logit_lens_with_skip( + self, prompt: str, skip_layers: set[int], token_id: int + ) -> dict[str, Any]: + """Run logit lens with MoE skipped at specified layers. + + Returns probability and rank of token_id at each layer. + """ + from chuk_lazarus.introspection.hooks import CaptureConfig, ModelHooks + from chuk_lazarus.introspection.logit_lens import LogitLens + + experiment = self + original_call = self._original_mlp_call + + def patched_mlp(mlp_self: Any, x: mx.array) -> mx.array: + layer_idx = -1 + for i, layer in enumerate(experiment.model.model.layers): + if hasattr(layer, "mlp") and layer.mlp is mlp_self: + layer_idx = i + break + if layer_idx in skip_layers: + return mx.zeros_like(x) + return original_call(mlp_self, x) + + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + + hooks = ModelHooks(self.model) + hooks.configure( + CaptureConfig( + layers="all", + capture_hidden_states=True, + positions="last", + ) + ) + + try: + self._mlp_class.__call__ = patched_mlp + hooks.forward(input_ids) + finally: + self._mlp_class.__call__ = self._original_mlp_call + + lens = LogitLens(hooks, self.tokenizer) + evolution = lens.track_token(token_id, position=-1, top_k_for_rank=200) + + # Find emergence points + emergence_top1 = evolution.emergence_layer + emergence_top5 = None + threshold_50 = None + + for layer, rank, prob in zip( + evolution.layers, evolution.ranks, evolution.probabilities + ): + if emergence_top5 is None and rank is not None and rank <= 5: + emergence_top5 = layer + if threshold_50 is None and prob >= 0.50: + threshold_50 = layer + + return { + "layers": evolution.layers, + "probabilities": evolution.probabilities, + "ranks": evolution.ranks, + "emergence_top1": emergence_top1, + "emergence_top5": emergence_top5, + "threshold_50pct": threshold_50, + } + + async def run_condition(self, name: str, skip_layers: list[int]): + skip_set = set(skip_layers) + skip_str = f"skip {skip_layers}" if skip_layers else "no skip" + logger.info(f"\n {name}: {skip_str}") + + loop = asyncio.get_event_loop() + + for fact in FACTS: + prompt = fact["prompt"] + keyword = fact["expected_keyword"] + + # Discover the correct token under normal conditions + token_id, token_str = self._discover_fact_token(prompt) + + # Generate with skip + if skip_layers: + text = await loop.run_in_executor( + None, self._generate_with_skip, prompt, skip_set, + ) + else: + text = await loop.run_in_executor( + None, self._generate, prompt, + ) + mx.eval(mx.zeros(1)) + + preserved = keyword.lower() in text.lower() + rep = compute_repetition_ratio(text) + + # Logit lens with skip + lens_data = self._run_logit_lens_with_skip(prompt, skip_set, token_id) + + logger.info( + f" {prompt[:38]:38} | " + f"{'ok' if preserved else 'LOST':>4} | " + f"top1@L{lens_data['emergence_top1']} | " + f">50%@L{lens_data['threshold_50pct']} | " + f"{text[:40]}" + ) + + self.results.append({ + "condition": name, + "skip_layers": skip_layers, + "prompt": prompt, + "expected_keyword": keyword, + "discovered_token": token_str, + "discovered_token_id": token_id, + "text": text[:120], + "fact_preserved": preserved, + "repetition_ratio": rep, + "emergence_top1": lens_data["emergence_top1"], + "emergence_top5": lens_data["emergence_top5"], + "threshold_50pct": lens_data["threshold_50pct"], + "probability_curve": { + str(l): round(p, 6) + for l, p in zip(lens_data["layers"], lens_data["probabilities"]) + }, + }) + + def _print_summary(self): + print("\n" + "=" * 95) + print("LAYER SKIP AT EMERGENCE POINT - RESULTS") + print("=" * 95) + + by_cond: dict[str, list[dict]] = defaultdict(list) + for r in self.results: + by_cond[r["condition"]].append(r) + + # Overview table + print( + f"\n{'Condition':<22} | {'Skip':>18} | " + f"{'Facts':>5} | {'AvgTop1':>7} | {'Avg>50%':>7} | {'AvgRep':>7}" + ) + print("-" * 95) + + for name in CONDITIONS: + results = by_cond.get(name, []) + if not results: + continue + + facts_ok = sum(1 for r in results if r["fact_preserved"]) + top1s = [r["emergence_top1"] for r in results if r["emergence_top1"] is not None] + th50s = [r["threshold_50pct"] for r in results if r["threshold_50pct"] is not None] + avg_rep = sum(r["repetition_ratio"] for r in results) / len(results) + + avg_top1 = f"L{sum(top1s)/len(top1s):.1f}" if top1s else "-" + avg_th50 = f"L{sum(th50s)/len(th50s):.1f}" if th50s else "-" + skip_str = str(CONDITIONS[name]) if CONDITIONS[name] else "none" + + print( + f"{name:<22} | {skip_str:>18} | " + f"{facts_ok}/{len(results):>3} | {avg_top1:>7} | {avg_th50:>7} | " + f"{avg_rep:>7.3f}" + ) + + # Per-fact detail for key conditions + print("\n" + "-" * 95) + print("PER-FACT EMERGENCE SHIFT") + print("-" * 95) + + for fact in FACTS: + prompt_short = fact["prompt"][:35] + print(f"\n {prompt_short}:") + + for name in CONDITIONS: + results = by_cond.get(name, []) + match = [r for r in results if r["prompt"] == fact["prompt"]] + if not match: + continue + r = match[0] + status = "ok" if r["fact_preserved"] else "LOST" + top1 = f"L{r['emergence_top1']}" if r["emergence_top1"] is not None else "never" + th50 = f"L{r['threshold_50pct']}" if r["threshold_50pct"] is not None else "never" + print( + f" {name:<22}: [{status:>4}] top1={top1:<6} >50%={th50:<6} " + f"| {r['text'][:45]}" + ) + + # Average probability curves for key conditions + print("\n" + "-" * 95) + print("AVERAGE PROBABILITY CURVE COMPARISON") + print("-" * 95) + + key_conditions = ["normal", "skip_L20_L21", "skip_L19_L20_L21", "skip_L22_L23"] + header = f"{'Layer':>5}" + for name in key_conditions: + if name in by_cond: + header += f" | {name:>18}" + print(header) + print("-" * (6 + 21 * len(key_conditions))) + + for layer in range(24): + row = f"L{layer:>3}:" + for name in key_conditions: + results = by_cond.get(name, []) + if not results: + continue + probs = [] + for r in results: + curve = r["probability_curve"] + if str(layer) in curve: + probs.append(curve[str(layer)]) + avg_p = sum(probs) / len(probs) if probs else 0.0 + bar_len = int(avg_p * 12) + bar = "#" * bar_len + row += f" | {avg_p:>6.4f} {bar:<11}" + print(row) + + # Key findings + print("\n" + "=" * 95) + print("KEY FINDINGS") + print("=" * 95) + + normal_facts = sum(1 for r in by_cond.get("normal", []) if r["fact_preserved"]) + skip20_facts = sum(1 for r in by_cond.get("skip_L20", []) if r["fact_preserved"]) + skip21_facts = sum(1 for r in by_cond.get("skip_L21", []) if r["fact_preserved"]) + skip2021_facts = sum(1 for r in by_cond.get("skip_L20_L21", []) if r["fact_preserved"]) + skip192021_facts = sum(1 for r in by_cond.get("skip_L19_L20_L21", []) if r["fact_preserved"]) + + print(f"\n Fact preservation:") + print(f" normal: {normal_facts}/{len(FACTS)}") + print(f" skip L20: {skip20_facts}/{len(FACTS)}") + print(f" skip L21: {skip21_facts}/{len(FACTS)}") + print(f" skip L20+L21: {skip2021_facts}/{len(FACTS)}") + print(f" skip L19+L20+L21: {skip192021_facts}/{len(FACTS)}") + + if skip2021_facts == normal_facts: + print("\n FINDING: Skipping emergence layers does NOT kill facts.") + print(" Facts crystallize at later layers when L20-L21 are removed.") + print(" The residual stream is robust - fact computation is deferrable.") + elif skip2021_facts == 0: + print("\n FINDING: Skipping emergence layers KILLS all facts.") + print(" L20-L21 are the critical bottleneck for fact crystallization.") + print(" The residual stream cannot compensate without these layers.") + else: + print(f"\n FINDING: Partial survival ({skip2021_facts}/{len(FACTS)}).") + print(" Some facts can defer to later layers, others cannot.") + print(" Fact difficulty correlates with emergence robustness.") + + print("=" * 95) + + def _save_results(self) -> Path: + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = results_dir / f"layer_skip_emergence_{timestamp}.json" + + with open(output_path, "w") as f: + json.dump( + { + "metadata": { + "experiment": "layer_skip_emergence", + "model": "openai/gpt-oss-20b", + "timestamp": timestamp, + "num_facts": len(FACTS), + "conditions": { + k: {"skip_layers": v, "n_skipped": len(v)} + for k, v in CONDITIONS.items() + }, + "description": ( + "Tests whether fact emergence layers (L20-21) are " + "necessary by zeroing MoE FFN output at those layers. " + "Attention still runs; residual passes through unchanged." + ), + "prior_results": { + "fact_emergence_avg_top1": "L20.8", + "knowledge_ablation": "0/8 facts break at any layer", + }, + }, + "results": self.results, + }, + f, + indent=2, + ) + logger.info(f"\nResults saved to {output_path}") + return output_path + + async def run(self): + await self.setup() + + n_passes = len(CONDITIONS) * len(FACTS) + logger.info("=" * 70) + logger.info("LAYER SKIP AT EMERGENCE POINT") + logger.info(f" Conditions: {len(CONDITIONS)}") + logger.info(f" Facts: {len(FACTS)}") + logger.info(f" Total passes: ~{n_passes} generate + {n_passes} logit lens") + logger.info("=" * 70) + + for name, skip_layers in CONDITIONS.items(): + await self.run_condition(name, skip_layers) + + self._save_results() + self._print_summary() + + +async def main(): + experiment = LayerSkipEmergence() + await experiment.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/experiments/expert_function_classification/memory_bank_injection_point.py b/experiments/expert_function_classification/memory_bank_injection_point.py new file mode 100644 index 00000000..4489bcec --- /dev/null +++ b/experiments/expert_function_classification/memory_bank_injection_point.py @@ -0,0 +1,678 @@ +#!/usr/bin/env python3 +"""Memory Bank Injection Point Experiment. + +Prior experiments established: + - Facts crystallize at L20-21 in the residual stream (logit lens) + - L20-21 are important but not irreplaceable (5/7 facts survive skip) + - Attention focuses on entity at L19/L21 (1.3x increase) + - Memory bank injection + 6-7 learned layers = 100% fact preservation + +This experiment answers: WHERE does memory bank injection influence the +residual stream? When we prepend [Memory Bank] facts to a prompt, does +the fact appear EARLIER in the residual stream (bypassing the L20-21 +computation), or does it simply reinforce what the model already computes? + +Method: + For each fact, we run two conditions: + 1. BARE: just the prompt ("The capital of France is") + 2. MB: memory bank + prompt ("[Memory Bank]\n- France | capital | Paris\n...") + + At each layer, we capture hidden states (last position) and compare: + - Cosine distance between bare and MB hidden states + - Fact token probability (logit lens) under each condition + - Probability lift (MB - bare) at each layer + - Emergence point shift (does MB cause earlier emergence?) + +Predictions: + - MB should cause earlier fact emergence (facts enter via attention at L0-L4) + - Residual delta should peak at early-to-mid layers where MB context is encoded + - By L20+, bare and MB residual streams should converge (both carry the fact) + +Run: python experiments/expert_function_classification/memory_bank_injection_point.py +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + +import mlx.core as mx + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +FACTS = [ + { + "prompt": "The capital of France is", + "expected_keyword": "Paris", + "mb_entry": "France | capital | Paris", + }, + { + "prompt": "The chemical symbol for gold is", + "expected_keyword": "Au", + "mb_entry": "Gold | chemical symbol | Au", + }, + { + "prompt": "The author of Romeo and Juliet is", + "expected_keyword": "Shakespeare", + "mb_entry": "Romeo and Juliet | author | William Shakespeare", + }, + { + "prompt": "The CEO of Microsoft is", + "expected_keyword": "Nadella", + "mb_entry": "Microsoft | CEO | Satya Nadella", + }, + { + "prompt": "The capital of Japan is", + "expected_keyword": "Tokyo", + "mb_entry": "Japan | capital | Tokyo", + }, + { + "prompt": "The chemical symbol for silver is", + "expected_keyword": "Ag", + "mb_entry": "Silver | chemical symbol | Ag", + }, + { + "prompt": "The capital of Australia is", + "expected_keyword": "Canberra", + "mb_entry": "Australia | capital | Canberra", + }, +] + + +def build_memory_bank_prompt(question: str, mb_entries: list[str]) -> str: + """Build a prompt with memory bank injection.""" + mb_block = "\n".join(f"- {entry}" for entry in mb_entries) + return ( + f"[Memory Bank]\n" + f"{mb_block}\n" + f"[End Memory Bank]\n\n" + f"Using the memory bank above, answer: {question}\n" + f"Answer:" + ) + + +class MemoryBankInjectionPoint: + """Compare residual streams between bare and memory-bank-injected prompts.""" + + def __init__(self): + self.model = None + self.tokenizer = None + + async def setup(self): + from chuk_lazarus.introspection.moe.expert_router import ExpertRouter + + logger.info("Loading model: openai/gpt-oss-20b") + router = await ExpertRouter.from_pretrained("openai/gpt-oss-20b") + self.model = router._model + self.tokenizer = router._tokenizer + + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + mx.eval(self.model.parameters()) + + self.num_layers = len(self.model.model.layers) + logger.info(f"Model loaded: {self.num_layers} layers. Ready.") + + def _discover_fact_token(self, prompt: str) -> tuple[int, str]: + """Get the model's actual predicted next token for this prompt.""" + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + output = self.model(input_ids) + logits = output.logits if hasattr(output, "logits") else output + next_token_id = int(mx.argmax(logits[0, -1, :]).item()) + decoded = self.tokenizer.decode([next_token_id]) + return next_token_id, decoded + + def _run_logit_lens(self, prompt: str) -> dict[str, Any]: + """Run logit lens at all layers. Returns hooks, lens, and hidden states.""" + from chuk_lazarus.introspection.hooks import CaptureConfig, ModelHooks + from chuk_lazarus.introspection.logit_lens import LogitLens + + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + + hooks = ModelHooks(self.model) + hooks.configure( + CaptureConfig( + layers="all", + capture_hidden_states=True, + capture_pre_norm=True, + positions="last", + ) + ) + hooks.forward(input_ids) + + lens = LogitLens(hooks, self.tokenizer) + return { + "hooks": hooks, + "lens": lens, + "input_ids": input_ids, + } + + def _track_token( + self, lens_result: dict, token_id: int + ) -> dict[str, Any]: + """Track a token's probability and rank through all layers.""" + from chuk_lazarus.introspection.logit_lens import LogitLens + + lens: LogitLens = lens_result["lens"] + evolution = lens.track_token(token_id, position=-1, top_k_for_rank=200) + + emergence_top1 = evolution.emergence_layer + emergence_top5 = None + threshold_10pct = None + threshold_50pct = None + + for layer, rank, prob in zip( + evolution.layers, evolution.ranks, evolution.probabilities + ): + if emergence_top5 is None and rank is not None and rank <= 5: + emergence_top5 = layer + if threshold_10pct is None and prob >= 0.10: + threshold_10pct = layer + if threshold_50pct is None and prob >= 0.50: + threshold_50pct = layer + + return { + "layers": evolution.layers, + "probabilities": evolution.probabilities, + "ranks": evolution.ranks, + "emergence_top1": emergence_top1, + "emergence_top5": emergence_top5, + "threshold_10pct": threshold_10pct, + "threshold_50pct": threshold_50pct, + } + + def _extract_hidden_states(self, hooks) -> dict[int, mx.array]: + """Extract hidden states at each layer from hooks. + + Returns {layer_idx: hidden_state} where hidden_state is 1D (hidden_dim). + Uses hooks.state.get_hidden_at_position() for clean extraction. + """ + states = {} + for layer_idx in hooks.state.captured_layers: + hs = hooks.state.get_hidden_at_position(layer_idx, position=-1) + if hs is not None: + states[layer_idx] = hs.reshape(-1) + return states + + def _cosine_distance(self, a: mx.array, b: mx.array) -> float: + """Compute cosine distance (1 - cosine_similarity) between two vectors.""" + dot = float(mx.sum(a * b).item()) + norm_a = float(mx.sqrt(mx.sum(a * a)).item()) + norm_b = float(mx.sqrt(mx.sum(b * b)).item()) + if norm_a == 0 or norm_b == 0: + return 1.0 + similarity = dot / (norm_a * norm_b) + return 1.0 - similarity + + def _l2_distance(self, a: mx.array, b: mx.array) -> float: + """Compute L2 distance between two vectors.""" + diff = a - b + return float(mx.sqrt(mx.sum(diff * diff)).item()) + + async def analyze_fact(self, fact: dict) -> dict[str, Any]: + """Compare bare vs MB residual streams for one fact.""" + prompt = fact["prompt"] + keyword = fact["expected_keyword"] + mb_entry = fact["mb_entry"] + + logger.info(f"\n Fact: {prompt}") + + # Build prompts + bare_prompt = prompt + all_mb_entries = [f["mb_entry"] for f in FACTS] + mb_prompt = build_memory_bank_prompt(prompt, all_mb_entries) + + logger.info(f" Bare prompt length: {len(self.tokenizer.encode(bare_prompt))} tokens") + logger.info(f" MB prompt length: {len(self.tokenizer.encode(mb_prompt))} tokens") + + # Discover fact tokens for each condition + bare_token_id, bare_token_str = self._discover_fact_token(bare_prompt) + mb_token_id, mb_token_str = self._discover_fact_token(mb_prompt) + + logger.info(f" Bare predicts: '{bare_token_str}' (id={bare_token_id})") + logger.info(f" MB predicts: '{mb_token_str}' (id={mb_token_id})") + + # Use the bare token as the tracking target (consistent across conditions) + track_token_id = bare_token_id + track_token_str = bare_token_str + + # Run logit lens for both conditions + bare_lens = self._run_logit_lens(bare_prompt) + mb_lens = self._run_logit_lens(mb_prompt) + + # Track the fact token through both residual streams + bare_tracking = self._track_token(bare_lens, track_token_id) + mb_tracking = self._track_token(mb_lens, track_token_id) + + logger.info( + f" Bare emergence: top5@L{bare_tracking['emergence_top5']}, " + f"top1@L{bare_tracking['emergence_top1']}, " + f">50%@L{bare_tracking['threshold_50pct']}" + ) + logger.info( + f" MB emergence: top5@L{mb_tracking['emergence_top5']}, " + f"top1@L{mb_tracking['emergence_top1']}, " + f">50%@L{mb_tracking['threshold_50pct']}" + ) + + # Extract hidden states and compute distances + bare_states = self._extract_hidden_states(bare_lens["hooks"]) + mb_states = self._extract_hidden_states(mb_lens["hooks"]) + + cosine_distances = {} + l2_distances = {} + + for layer_idx in sorted(bare_states.keys()): + if layer_idx in mb_states: + cos_d = self._cosine_distance(bare_states[layer_idx], mb_states[layer_idx]) + l2_d = self._l2_distance(bare_states[layer_idx], mb_states[layer_idx]) + cosine_distances[layer_idx] = cos_d + l2_distances[layer_idx] = l2_d + + # Compute probability lift at each layer + probability_lift = {} + for i, layer in enumerate(bare_tracking["layers"]): + bare_prob = bare_tracking["probabilities"][i] + # Find corresponding MB probability + mb_prob = 0.0 + if layer in mb_tracking["layers"]: + mb_idx = mb_tracking["layers"].index(layer) + mb_prob = mb_tracking["probabilities"][mb_idx] + probability_lift[layer] = mb_prob - bare_prob + + # Log key layers + for l in [0, 4, 8, 12, 15, 19, 20, 21, 22, 23]: + if l in cosine_distances: + bare_p = bare_tracking["probabilities"][bare_tracking["layers"].index(l)] if l in bare_tracking["layers"] else 0 + mb_p = mb_tracking["probabilities"][mb_tracking["layers"].index(l)] if l in mb_tracking["layers"] else 0 + lift = probability_lift.get(l, 0) + logger.info( + f" L{l:>2}: cos_dist={cosine_distances[l]:.4f}, " + f"bare={bare_p:.4f}, mb={mb_p:.4f}, lift={lift:+.4f}" + ) + + # Emergence shift + bare_top1 = bare_tracking["emergence_top1"] + mb_top1 = mb_tracking["emergence_top1"] + emergence_shift = None + if bare_top1 is not None and mb_top1 is not None: + emergence_shift = bare_top1 - mb_top1 # positive = MB is earlier + + return { + "prompt": prompt, + "expected_keyword": keyword, + "bare_token": bare_token_str, + "mb_token": mb_token_str, + "track_token": track_token_str, + "track_token_id": track_token_id, + "bare_emergence": { + "top5": bare_tracking["emergence_top5"], + "top1": bare_tracking["emergence_top1"], + "threshold_10pct": bare_tracking["threshold_10pct"], + "threshold_50pct": bare_tracking["threshold_50pct"], + }, + "mb_emergence": { + "top5": mb_tracking["emergence_top5"], + "top1": mb_tracking["emergence_top1"], + "threshold_10pct": mb_tracking["threshold_10pct"], + "threshold_50pct": mb_tracking["threshold_50pct"], + }, + "emergence_shift_top1": emergence_shift, + "cosine_distance_by_layer": { + str(k): round(v, 6) for k, v in cosine_distances.items() + }, + "l2_distance_by_layer": { + str(k): round(v, 4) for k, v in l2_distances.items() + }, + "bare_probability_by_layer": { + str(l): round(p, 6) + for l, p in zip(bare_tracking["layers"], bare_tracking["probabilities"]) + }, + "mb_probability_by_layer": { + str(l): round(p, 6) + for l, p in zip(mb_tracking["layers"], mb_tracking["probabilities"]) + }, + "probability_lift_by_layer": { + str(k): round(v, 6) for k, v in probability_lift.items() + }, + } + + def _compute_summary(self, fact_results: list[dict]) -> dict[str, Any]: + """Compute aggregate statistics across all facts.""" + valid = [r for r in fact_results if "error" not in r] + + # Average cosine distance by layer + avg_cosine: dict[int, list[float]] = defaultdict(list) + avg_lift: dict[int, list[float]] = defaultdict(list) + avg_bare_prob: dict[int, list[float]] = defaultdict(list) + avg_mb_prob: dict[int, list[float]] = defaultdict(list) + + for r in valid: + for layer_str, val in r["cosine_distance_by_layer"].items(): + avg_cosine[int(layer_str)].append(val) + for layer_str, val in r["probability_lift_by_layer"].items(): + avg_lift[int(layer_str)].append(val) + for layer_str, val in r["bare_probability_by_layer"].items(): + avg_bare_prob[int(layer_str)].append(val) + for layer_str, val in r["mb_probability_by_layer"].items(): + avg_mb_prob[int(layer_str)].append(val) + + avg_cosine_curve = { + l: round(sum(v) / len(v), 6) for l, v in sorted(avg_cosine.items()) + } + avg_lift_curve = { + l: round(sum(v) / len(v), 6) for l, v in sorted(avg_lift.items()) + } + avg_bare_curve = { + l: round(sum(v) / len(v), 6) for l, v in sorted(avg_bare_prob.items()) + } + avg_mb_curve = { + l: round(sum(v) / len(v), 6) for l, v in sorted(avg_mb_prob.items()) + } + + # Peak cosine distance layer + if avg_cosine_curve: + peak_cos_layer = max(avg_cosine_curve, key=avg_cosine_curve.get) + peak_cos_value = avg_cosine_curve[peak_cos_layer] + else: + peak_cos_layer = None + peak_cos_value = None + + # Peak probability lift layer + if avg_lift_curve: + peak_lift_layer = max(avg_lift_curve, key=avg_lift_curve.get) + peak_lift_value = avg_lift_curve[peak_lift_layer] + else: + peak_lift_layer = None + peak_lift_value = None + + # Emergence shifts + shifts_top1 = [r["emergence_shift_top1"] for r in valid if r["emergence_shift_top1"] is not None] + bare_top1s = [r["bare_emergence"]["top1"] for r in valid if r["bare_emergence"]["top1"] is not None] + mb_top1s = [r["mb_emergence"]["top1"] for r in valid if r["mb_emergence"]["top1"] is not None] + + avg_shift = round(sum(shifts_top1) / len(shifts_top1), 1) if shifts_top1 else None + avg_bare_emergence = round(sum(bare_top1s) / len(bare_top1s), 1) if bare_top1s else None + avg_mb_emergence = round(sum(mb_top1s) / len(mb_top1s), 1) if mb_top1s else None + + # Phase averages for cosine distance + early_cos = [avg_cosine_curve.get(l, 0) for l in range(0, 10)] + mid_cos = [avg_cosine_curve.get(l, 0) for l in range(10, 18)] + late_cos = [avg_cosine_curve.get(l, 0) for l in range(18, 24)] + + avg_early_cos = sum(early_cos) / len(early_cos) if early_cos else 0 + avg_mid_cos = sum(mid_cos) / len(mid_cos) if mid_cos else 0 + avg_late_cos = sum(late_cos) / len(late_cos) if late_cos else 0 + + return { + "num_facts": len(valid), + "emergence_comparison": { + "avg_bare_top1": avg_bare_emergence, + "avg_mb_top1": avg_mb_emergence, + "avg_shift": avg_shift, + "per_fact_shifts": shifts_top1, + "interpretation": ( + f"MB shifts emergence by {avg_shift:+.1f} layers on average " + f"(bare: L{avg_bare_emergence}, MB: L{avg_mb_emergence})" + if avg_shift is not None else "No shift data" + ), + }, + "residual_distance": { + "avg_cosine_by_layer": avg_cosine_curve, + "peak_cosine_layer": peak_cos_layer, + "peak_cosine_value": peak_cos_value, + "phase_averages": { + "early_L0_L9": round(avg_early_cos, 6), + "mid_L10_L17": round(avg_mid_cos, 6), + "late_L18_L23": round(avg_late_cos, 6), + }, + }, + "probability_lift": { + "avg_lift_by_layer": avg_lift_curve, + "peak_lift_layer": peak_lift_layer, + "peak_lift_value": peak_lift_value, + }, + "avg_bare_probability": avg_bare_curve, + "avg_mb_probability": avg_mb_curve, + } + + def _print_summary(self, summary: dict, fact_results: list[dict]): + valid = [r for r in fact_results if "error" not in r] + + print("\n" + "=" * 100) + print("MEMORY BANK INJECTION POINT - RESULTS") + print("=" * 100) + + # Per-fact emergence comparison + print( + f"\n{'Prompt':<36} | {'Bare':>4} | {'MB':>4} | " + f"{'Token':<10} | {'MB Token':<10} | {'Shift':>5}" + ) + print("-" * 100) + + for r in valid: + prompt_short = r["prompt"][:34] + bare_top1 = f"L{r['bare_emergence']['top1']}" if r["bare_emergence"]["top1"] is not None else "-" + mb_top1 = f"L{r['mb_emergence']['top1']}" if r["mb_emergence"]["top1"] is not None else "-" + shift = r["emergence_shift_top1"] + shift_str = f"{shift:+d}" if shift is not None else "-" + print( + f"{prompt_short:<36} | {bare_top1:>4} | {mb_top1:>4} | " + f"{r['bare_token']:<10} | {r['mb_token']:<10} | {shift_str:>5}" + ) + + # Emergence summary + ec = summary["emergence_comparison"] + print(f"\n Average bare emergence (top-1): L{ec['avg_bare_top1']}") + print(f" Average MB emergence (top-1): L{ec['avg_mb_top1']}") + print(f" Average shift: {ec['avg_shift']:+.1f} layers") + + # Cosine distance curve + print("\n" + "-" * 100) + print("RESIDUAL STREAM DISTANCE (bare vs MB) - Cosine Distance by Layer") + print("-" * 100) + + cos_curve = summary["residual_distance"]["avg_cosine_by_layer"] + max_cos = max(cos_curve.values()) if cos_curve else 1 + for layer in sorted(cos_curve.keys()): + val = cos_curve[layer] + bar_len = int((val / max(max_cos, 0.001)) * 40) + bar = "#" * bar_len + marker = "" + if layer == summary["residual_distance"]["peak_cosine_layer"]: + marker = " <- peak divergence" + print(f" L{layer:>2}: {val:>8.5f} |{bar}{marker}") + + phases = summary["residual_distance"]["phase_averages"] + print(f"\n Phase averages:") + print(f" Early (L0-L9): {phases['early_L0_L9']:.5f}") + print(f" Mid (L10-L17): {phases['mid_L10_L17']:.5f}") + print(f" Late (L18-L23): {phases['late_L18_L23']:.5f}") + + # Probability comparison + print("\n" + "-" * 100) + print("FACT PROBABILITY: BARE vs MB") + print("-" * 100) + + bare_curve = summary["avg_bare_probability"] + mb_curve = summary["avg_mb_probability"] + lift_curve = summary["probability_lift"]["avg_lift_by_layer"] + + print(f" {'Layer':>5} | {'Bare':>8} | {'MB':>8} | {'Lift':>8} | {'Bare':15} | {'MB':15}") + print(" " + "-" * 90) + + for layer in sorted(bare_curve.keys()): + bare_p = bare_curve.get(layer, 0) + mb_p = mb_curve.get(layer, 0) + lift = lift_curve.get(layer, 0) + bare_bar = "#" * int(bare_p * 15) + mb_bar = "#" * int(mb_p * 15) + print( + f" L{layer:>3}: {bare_p:>8.4f} | {mb_p:>8.4f} | {lift:>+8.4f} | " + f"{bare_bar:<15} | {mb_bar:<15}" + ) + + # Peak lift + pl = summary["probability_lift"] + print(f"\n Peak probability lift: L{pl['peak_lift_layer']} ({pl['peak_lift_value']:+.4f})") + + # Key findings + print("\n" + "=" * 100) + print("KEY FINDINGS") + print("=" * 100) + + shift = ec["avg_shift"] + if shift is not None and shift > 2: + print( + f"\n MB shifts fact emergence {shift:+.1f} layers EARLIER." + ) + print( + " Memory bank injection bypasses the normal L20-21 crystallization," + ) + print( + " providing facts via attention at earlier layers." + ) + elif shift is not None and shift > 0: + print( + f"\n MB shifts fact emergence modestly earlier ({shift:+.1f} layers)." + ) + print( + " Memory bank provides some acceleration but doesn't fundamentally" + ) + print( + " change the computation pathway." + ) + elif shift is not None and abs(shift) <= 0.5: + print( + f"\n MB does NOT shift emergence ({shift:+.1f} layers)." + ) + print( + " Facts emerge at the same layers regardless of memory bank." + ) + print( + " MB may work by reinforcing the residual stream rather than" + ) + print( + " providing an alternative pathway." + ) + elif shift is not None: + print( + f"\n MB shifts emergence LATER ({shift:+.1f} layers) -- unexpected." + ) + print( + " The memory bank may introduce interference or distraction" + ) + print( + " that delays fact crystallization." + ) + + peak_cos = summary["residual_distance"]["peak_cosine_layer"] + if peak_cos is not None: + print( + f"\n Residual streams diverge most at L{peak_cos}." + ) + if peak_cos < 10: + print( + " Early divergence: MB context is encoded in early layers," + ) + print( + " then the representations converge as the model processes." + ) + elif peak_cos < 18: + print( + " Mid-network divergence: MB influence peaks during the" + ) + print( + " representational buildup phase." + ) + else: + print( + " Late divergence: MB influence peaks at the crystallization" + ) + print( + " layers, suggesting it modifies the final computation." + ) + + print("=" * 100) + + def _save_results(self, results: dict) -> Path: + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = results_dir / f"memory_bank_injection_point_{timestamp}.json" + + with open(output_path, "w") as f: + json.dump(results, f, indent=2, default=str) + logger.info(f"\nResults saved to {output_path}") + return output_path + + async def run(self): + await self.setup() + + logger.info("=" * 70) + logger.info("MEMORY BANK INJECTION POINT") + logger.info(f" Facts: {len(FACTS)}") + logger.info(f" Conditions: bare vs memory bank") + logger.info(f" Measures: cosine distance, probability lift, emergence shift") + logger.info("=" * 70) + + fact_results = [] + for fact in FACTS: + result = await self.analyze_fact(fact) + fact_results.append(result) + + summary = self._compute_summary(fact_results) + + output = { + "metadata": { + "experiment": "memory_bank_injection_point", + "model": "openai/gpt-oss-20b", + "timestamp": datetime.now().isoformat(), + "num_facts": len(FACTS), + "description": ( + "Compares residual streams between bare prompts and " + "memory-bank-injected prompts at each layer. Measures " + "cosine distance, fact probability lift, and emergence " + "point shift to determine WHERE memory bank injection " + "influences the computation." + ), + "method": ( + "For each fact, runs logit lens under two conditions: " + "(1) bare prompt, (2) [Memory Bank]...[End Memory Bank] + prompt. " + "Captures hidden states at all 24 layers (last position) and " + "computes cosine distance between bare/MB representations, " + "plus tracks fact token probability under both conditions." + ), + "prior_results": { + "fact_emergence_avg_top1": "L20.8 (bare, logit lens)", + "layer_skip_L20_L21": "5/7 facts survive", + "entity_attention_increase": "1.3x at emergence", + "min_viable_routing_with_mb": "6-7 layers + MB = 100%", + }, + }, + "summary": summary, + "fact_results": fact_results, + } + + self._save_results(output) + self._print_summary(summary, fact_results) + + +async def main(): + experiment = MemoryBankInjectionPoint() + await experiment.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/experiments/expert_function_classification/residual_fact_emergence.py b/experiments/expert_function_classification/residual_fact_emergence.py new file mode 100644 index 00000000..636cf5f0 --- /dev/null +++ b/experiments/expert_function_classification/residual_fact_emergence.py @@ -0,0 +1,603 @@ +#!/usr/bin/env python3 +"""Residual Stream Fact Emergence Experiment. + +Knowledge ablation showed facts survive full top-4 expert ablation at +every tested layer (L8, L12, L16, L20). Minimum viable routing showed +6-7 learned layers + memory bank = 100% fact preservation. + +This experiment answers: WHERE in the residual stream do facts actually +emerge? Using logit lens, we project intermediate hidden states to vocab +logits at every layer and track when the correct answer token first +appears (rank-1) and when it becomes confident (>50% probability). + +If facts emerge early (L4-L8) but expert ablation at those layers doesn't +break them, that proves facts propagate through the residual stream +independently of expert computation. + +We test three conditions: + 1. Normal: full model, logit lens at all 24 layers + 2. Ablated emergence: ablate top-4 experts AT the emergence layer + 3. Skip emergence: skip the entire MoE FFN at the emergence layer + +This directly connects your knowledge_ablation finding (facts never break) +to a mechanistic explanation (residual stream carries facts, experts add +fluency). + +Run: python experiments/expert_function_classification/residual_fact_emergence.py +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + +import mlx.core as mx + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +# ============================================================================= +# Data - same 8 facts from prior experiments for comparability +# ============================================================================= + +FACTS = [ + {"prompt": "The capital of France is", "expected_keyword": "Paris"}, + {"prompt": "The chemical symbol for gold is", "expected_keyword": "Au"}, + {"prompt": "The author of Romeo and Juliet is", "expected_keyword": "Shakespeare"}, + {"prompt": "The speed of light is approximately", "expected_keyword": "299"}, + {"prompt": "The CEO of Microsoft is", "expected_keyword": "Nadella"}, + {"prompt": "The capital of Japan is", "expected_keyword": "Tokyo"}, + {"prompt": "The chemical symbol for silver is", "expected_keyword": "Ag"}, + {"prompt": "The capital of Australia is", "expected_keyword": "Canberra"}, +] + + +# ============================================================================= +# Experiment +# ============================================================================= + + +class ResidualFactEmergence: + """Track where facts emerge in the residual stream via logit lens.""" + + def __init__(self): + self.model = None + self.tokenizer = None + self.hooks = None + + async def setup(self): + """Load model and set up hooks.""" + from chuk_lazarus.introspection.moe import ExpertRouter + + logger.info("Loading model: openai/gpt-oss-20b") + router = await ExpertRouter.from_pretrained("openai/gpt-oss-20b") + self.model = router._model + self.tokenizer = router._tokenizer + self._router = router + + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + mx.eval(self.model.parameters()) + + info = router._info + self.num_layers = len(info.moe_layers) + logger.info( + f"Model loaded: {info.num_experts} experts/layer, " + f"{self.num_layers} MoE layers, top-{info.num_experts_per_tok}" + ) + + def _discover_fact_token(self, prompt: str) -> tuple[int, str]: + """Discover the actual token the model predicts for this prompt. + + Instead of guessing token IDs (which fails due to BPE space + prefixes like 'Ġ', '▁', etc.), we run a forward pass and take + the argmax of the final logits. This IS the fact token. + + Returns (token_id, decoded_token_string). + """ + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + output = self.model(input_ids) + logits = output.logits if hasattr(output, "logits") else output + next_token_id = int(mx.argmax(logits[0, -1, :]).item()) + decoded = self.tokenizer.decode([next_token_id]) + return next_token_id, decoded + + def _run_logit_lens(self, prompt: str) -> dict[str, Any]: + """Run logit lens at all layers, return per-layer top predictions. + + Returns dict with: + - layer_predictions: {layer_idx: [(token, prob), ...]} + - hidden_states captured for further analysis + """ + from chuk_lazarus.introspection.hooks import CaptureConfig, ModelHooks + from chuk_lazarus.introspection.logit_lens import LogitLens + + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + + hooks = ModelHooks(self.model) + hooks.configure( + CaptureConfig( + layers="all", + capture_hidden_states=True, + capture_pre_norm=True, + positions="last", # Only last position (prediction point) + ) + ) + hooks.forward(input_ids) + + lens = LogitLens(hooks, self.tokenizer) + return { + "hooks": hooks, + "lens": lens, + "input_ids": input_ids, + } + + def _track_fact_token( + self, + lens_result: dict, + token_id: int, + token_str: str, + ) -> dict[str, Any]: + """Track a specific token's emergence through residual stream layers.""" + from chuk_lazarus.introspection.logit_lens import LogitLens + + lens: LogitLens = lens_result["lens"] + + logger.info(f" Tracking token '{token_str}' (id={token_id})") + + evolution = lens.track_token(token_id, position=-1, top_k_for_rank=200) + + # Find key emergence points + emergence_top1 = evolution.emergence_layer # First layer at rank 1 + + emergence_top5 = None + emergence_top10 = None + threshold_10pct = None + threshold_50pct = None + + for layer, rank, prob in zip( + evolution.layers, evolution.ranks, evolution.probabilities + ): + if emergence_top10 is None and rank is not None and rank <= 10: + emergence_top10 = layer + if emergence_top5 is None and rank is not None and rank <= 5: + emergence_top5 = layer + if threshold_10pct is None and prob >= 0.10: + threshold_10pct = layer + if threshold_50pct is None and prob >= 0.50: + threshold_50pct = layer + + return { + "token": token_str, + "token_id": token_id, + "layers": evolution.layers, + "probabilities": evolution.probabilities, + "ranks": evolution.ranks, + "emergence_top1": emergence_top1, + "emergence_top5": emergence_top5, + "emergence_top10": emergence_top10, + "threshold_10pct": threshold_10pct, + "threshold_50pct": threshold_50pct, + } + + def _get_top_predictions(self, lens_result: dict) -> dict[int, list[tuple[str, float]]]: + """Get top-5 predictions at each layer.""" + from chuk_lazarus.introspection.logit_lens import LogitLens + + lens: LogitLens = lens_result["lens"] + predictions = lens.get_layer_predictions(position=-1, top_k=5) + + return { + pred.layer_idx: list(zip(pred.top_tokens, pred.top_probs)) + for pred in predictions + } + + def _generate(self, prompt: str, max_tokens: int = 30) -> str: + """Generate text (baseline).""" + tokens = self.tokenizer(prompt, return_tensors="np") + input_ids = mx.array(tokens["input_ids"]) + + generated = [] + for _ in range(max_tokens): + output = self.model(input_ids) + next_token = mx.argmax(output.logits[0, -1, :]) + token_id = next_token.item() + if token_id == self.tokenizer.eos_token_id: + break + generated.append(token_id) + input_ids = mx.concatenate( + [input_ids, next_token.reshape(1, 1)], axis=1 + ) + return self.tokenizer.decode(generated).strip() + + async def analyze_fact(self, fact: dict) -> dict[str, Any]: + """Run full emergence analysis for one fact.""" + prompt = fact["prompt"] + expected_keyword = fact["expected_keyword"] + + logger.info(f"\n Fact: {prompt}") + + # 1. Discover the actual token the model predicts (no guessing BPE) + fact_token_id, fact_token_str = self._discover_fact_token(prompt) + logger.info(f" Model predicts: '{fact_token_str}' (id={fact_token_id})") + + # 2. Baseline generation (full sequence for correctness check) + baseline_text = self._generate(prompt) + baseline_correct = expected_keyword.lower() in baseline_text.lower() + logger.info( + f" Baseline: {'correct' if baseline_correct else 'WRONG'} | " + f"{baseline_text[:60]}" + ) + + # 3. Logit lens across all layers + lens_result = self._run_logit_lens(prompt) + + # 4. Track the ACTUAL predicted token through all layers + token_tracking = self._track_fact_token( + lens_result, fact_token_id, fact_token_str + ) + + logger.info( + f" Emergence: top10@L{token_tracking['emergence_top10']}, " + f"top5@L{token_tracking['emergence_top5']}, " + f"top1@L{token_tracking['emergence_top1']}" + ) + logger.info( + f" Threshold: >10%@L{token_tracking['threshold_10pct']}, " + f">50%@L{token_tracking['threshold_50pct']}" + ) + + # 4. Get top predictions at each layer for context + top_preds = self._get_top_predictions(lens_result) + + # 5. Log probability curve highlights + probs = token_tracking["probabilities"] + layers = token_tracking["layers"] + if probs: + peak_idx = max(range(len(probs)), key=lambda i: probs[i]) + logger.info( + f" Peak: L{layers[peak_idx]} at {probs[peak_idx]:.3f}" + ) + + # 6. Capture what's at the emergence layer (competing predictions) + emergence_layer = token_tracking["emergence_top1"] + competitors_at_emergence = {} + if emergence_layer is not None and emergence_layer in top_preds: + competitors_at_emergence = { + "layer": emergence_layer, + "predictions": [ + {"token": t, "prob": float(p)} + for t, p in top_preds[emergence_layer] + ], + } + + # 7. What's the residual doing at pre-emergence layers? + pre_emergence_predictions = {} + if emergence_layer is not None: + for check_layer in [0, emergence_layer // 2, max(0, emergence_layer - 1)]: + if check_layer in top_preds: + pre_emergence_predictions[check_layer] = [ + {"token": t, "prob": float(p)} + for t, p in top_preds[check_layer][:3] + ] + + return { + "prompt": prompt, + "discovered_token": fact_token_str, + "discovered_token_id": fact_token_id, + "expected_keyword": expected_keyword, + "baseline_text": baseline_text[:100], + "baseline_correct": baseline_correct, + "token_tracking": { + "token": token_tracking["token"], + "token_id": token_tracking["token_id"], + "emergence_top1": token_tracking["emergence_top1"], + "emergence_top5": token_tracking["emergence_top5"], + "emergence_top10": token_tracking["emergence_top10"], + "threshold_10pct": token_tracking["threshold_10pct"], + "threshold_50pct": token_tracking["threshold_50pct"], + "probability_curve": { + str(l): round(p, 6) + for l, p in zip( + token_tracking["layers"], + token_tracking["probabilities"], + ) + }, + "rank_curve": { + str(l): r + for l, r in zip( + token_tracking["layers"], + token_tracking["ranks"], + ) + }, + }, + "competitors_at_emergence": competitors_at_emergence, + "pre_emergence_predictions": pre_emergence_predictions, + } + + async def run(self) -> dict[str, Any]: + """Run the full experiment.""" + await self.setup() + + logger.info("=" * 70) + logger.info("RESIDUAL STREAM FACT EMERGENCE") + logger.info(f" Model: openai/gpt-oss-20b ({self.num_layers} layers)") + logger.info(f" Facts: {len(FACTS)}") + logger.info("=" * 70) + + fact_results = [] + for fact in FACTS: + result = await self.analyze_fact(fact) + fact_results.append(result) + + # Compute aggregate statistics + summary = self._compute_summary(fact_results) + + # Build output + output = { + "metadata": { + "experiment": "residual_fact_emergence", + "model": "openai/gpt-oss-20b", + "num_layers": self.num_layers, + "timestamp": datetime.now().isoformat(), + "num_facts": len(FACTS), + "description": ( + "Logit lens tracking of fact token emergence through " + "residual stream. Connects to knowledge_ablation finding " + "that facts survive full top-4 expert ablation." + ), + }, + "summary": summary, + "fact_results": fact_results, + } + + self._save_results(output) + self._print_summary(summary, fact_results) + + return output + + def _compute_summary(self, fact_results: list[dict]) -> dict[str, Any]: + """Compute aggregate statistics across all facts.""" + emergence_top1 = [] + emergence_top5 = [] + emergence_top10 = [] + threshold_10 = [] + threshold_50 = [] + baseline_correct = 0 + + for r in fact_results: + if r["baseline_correct"]: + baseline_correct += 1 + + t = r["token_tracking"] + if t["emergence_top1"] is not None: + emergence_top1.append(t["emergence_top1"]) + if t["emergence_top5"] is not None: + emergence_top5.append(t["emergence_top5"]) + if t["emergence_top10"] is not None: + emergence_top10.append(t["emergence_top10"]) + if t["threshold_10pct"] is not None: + threshold_10.append(t["threshold_10pct"]) + if t["threshold_50pct"] is not None: + threshold_50.append(t["threshold_50pct"]) + + def avg(lst): + return round(sum(lst) / len(lst), 1) if lst else None + + def med(lst): + if not lst: + return None + s = sorted(lst) + n = len(s) + return s[n // 2] if n % 2 == 1 else (s[n // 2 - 1] + s[n // 2]) / 2 + + # Probability at key layers (average across facts) + prob_at_layer = defaultdict(list) + for r in fact_results: + curve = r["token_tracking"]["probability_curve"] + for layer_str, prob in curve.items(): + prob_at_layer[int(layer_str)].append(prob) + + avg_prob_curve = { + layer: round(sum(probs) / len(probs), 6) + for layer, probs in sorted(prob_at_layer.items()) + } + + return { + "baseline_accuracy": f"{baseline_correct}/{len(fact_results)}", + "emergence_top1": { + "values": emergence_top1, + "mean": avg(emergence_top1), + "median": med(emergence_top1), + "min": min(emergence_top1) if emergence_top1 else None, + "max": max(emergence_top1) if emergence_top1 else None, + "count": len(emergence_top1), + }, + "emergence_top5": { + "values": emergence_top5, + "mean": avg(emergence_top5), + "median": med(emergence_top5), + }, + "emergence_top10": { + "values": emergence_top10, + "mean": avg(emergence_top10), + "median": med(emergence_top10), + }, + "threshold_10pct": { + "values": threshold_10, + "mean": avg(threshold_10), + "median": med(threshold_10), + }, + "threshold_50pct": { + "values": threshold_50, + "mean": avg(threshold_50), + "median": med(threshold_50), + }, + "avg_probability_by_layer": avg_prob_curve, + "interpretation": { + "early_emergence": ( + "Facts emerge before expert-heavy layers" + if emergence_top1 and avg(emergence_top1) < 12 + else "Facts emerge in mid-to-late layers" + if emergence_top1 and avg(emergence_top1) < 18 + else "Facts emerge in final layers" + if emergence_top1 + else "No emergence detected" + ), + "connection_to_ablation": ( + "Knowledge ablation showed 0/8 facts break at any layer. " + "If emergence is early, experts at later layers add fluency " + "but the residual stream already carries the fact." + ), + }, + } + + def _save_results(self, results: dict) -> None: + output_path = ( + Path(__file__).parent + / "results" + / f"residual_fact_emergence_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + ) + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w") as f: + json.dump(results, f, indent=2, default=str) + logger.info(f"\nResults saved to {output_path}") + + def _print_summary( + self, summary: dict, fact_results: list[dict] + ) -> None: + print("\n" + "=" * 80) + print("RESIDUAL STREAM FACT EMERGENCE - RESULTS") + print("=" * 80) + + print(f"\nBaseline accuracy: {summary['baseline_accuracy']}") + + # Per-fact emergence table + print("\n" + "-" * 80) + print( + f"{'Prompt':<42} | {'Top10':>5} | {'Top5':>5} | {'Top1':>5} | " + f"{'>10%':>5} | {'>50%':>5} | {'Token':<8}" + ) + print("-" * 80) + + for r in fact_results: + t = r["token_tracking"] + prompt_short = r["prompt"][:40] + top10 = f"L{t['emergence_top10']}" if t["emergence_top10"] is not None else "-" + top5 = f"L{t['emergence_top5']}" if t["emergence_top5"] is not None else "-" + top1 = f"L{t['emergence_top1']}" if t["emergence_top1"] is not None else "-" + th10 = f"L{t['threshold_10pct']}" if t["threshold_10pct"] is not None else "-" + th50 = f"L{t['threshold_50pct']}" if t["threshold_50pct"] is not None else "-" + tok = t["token"][:8] + print( + f"{prompt_short:<42} | {top10:>5} | {top5:>5} | {top1:>5} | " + f"{th10:>5} | {th50:>5} | {tok:<8}" + ) + + # Aggregate + print("\n" + "-" * 80) + print("AGGREGATE EMERGENCE LAYERS") + print("-" * 80) + + for metric in ["emergence_top10", "emergence_top5", "emergence_top1", + "threshold_10pct", "threshold_50pct"]: + data = summary[metric] + label = metric.replace("emergence_", "").replace("threshold_", ">") + print( + f" {label:<12}: mean=L{data['mean']}, " + f"median=L{data['median']}, " + f"range=[L{data.get('min', '?')}-L{data.get('max', '?')}]" + ) + + # Average probability curve (condensed) + print("\n" + "-" * 80) + print("AVERAGE FACT PROBABILITY BY LAYER") + print("-" * 80) + + curve = summary["avg_probability_by_layer"] + layers = sorted(curve.keys()) + + # Print as a compact bar chart + for layer in layers: + prob = curve[layer] + bar_len = int(prob * 50) + bar = "#" * bar_len + print(f" L{layer:>2}: {prob:>7.4f} |{bar}") + + # Key finding + print("\n" + "=" * 80) + print("KEY FINDINGS") + print("=" * 80) + print(f"\n {summary['interpretation']['early_emergence']}") + print(f"\n {summary['interpretation']['connection_to_ablation']}") + + e1 = summary["emergence_top1"] + if e1["mean"] is not None: + print(f"\n Average fact emergence (top-1): Layer {e1['mean']}") + if e1["mean"] < self.num_layers * 0.5: + print( + f" This is in the FIRST HALF of the network ({e1['mean']:.0f}/{self.num_layers})." + ) + print( + " Since knowledge ablation shows facts survive expert ablation at L8-L20," + ) + print( + " the residual stream is carrying facts INDEPENDENTLY of expert computation." + ) + print( + " Experts at post-emergence layers provide formatting/fluency, not facts." + ) + elif e1["mean"] < self.num_layers * 0.75: + print( + f" Facts emerge in the MIDDLE layers ({e1['mean']:.0f}/{self.num_layers})." + ) + print( + " Combined with knowledge ablation (facts survive expert ablation)," + ) + print( + " this suggests facts are built up gradually through the residual stream." + ) + else: + print( + f" Facts emerge LATE ({e1['mean']:.0f}/{self.num_layers})." + ) + print( + " But knowledge ablation shows they survive expert ablation even here." + ) + print( + " The residual stream computation is sufficient; experts refine but don't create." + ) + + # Connection to compression + print("\n COMPRESSION IMPLICATION:") + print( + " Minimum viable routing needs 6-7 learned layers for 100% MB recovery." + ) + if e1["mean"] is not None: + print( + f" Fact emergence at ~L{e1['mean']:.0f} suggests layers after emergence" + ) + print( + " contribute fluency (expert-dependent) rather than facts (residual-carried)." + ) + + print("=" * 80) + + +async def main(): + experiment = ResidualFactEmergence() + await experiment.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/experiments/expert_function_classification/results.md b/experiments/expert_function_classification/results.md new file mode 100644 index 00000000..bff85d44 --- /dev/null +++ b/experiments/expert_function_classification/results.md @@ -0,0 +1,612 @@ +# Expert Function Classification: Results + +**Model**: GPT-OSS 20B (24 MoE layers, 32 experts/layer, top-4 routing, ~21B params) +**Date**: 29 Jan - 2 Feb 2026 +**Experiments run**: 19 of 20 designed (15 classification + 4 residual stream mechanistic) + +--- + +## Executive Summary + +The original hypothesis -- that a significant fraction of MoE experts serve as identifiable "storage" units that can be individually externalized -- was **not supported**. Instead, we found something more fundamental: + +1. **Facts are not stored in experts.** Progressive ablation of all top-4 experts at any tested layer (L8, L12, L16, L20) breaks zero facts. Knowledge survives full expert removal. + +2. **Facts crystallize late in the residual stream.** Logit lens analysis shows fact tokens emerge at **L20-21** (out of 24 layers), with zero signal before L14. The residual stream constructs facts through distributed computation, not retrieval from individual components. + +3. **Routing layers can be frozen for compression.** 7 learned routing layers out of 24 (71% frozen) + memory bank injection achieves 100% fact preservation. This is a routing-based compression path, not an expert-removal path. + +The deliverable reframes as: + +> **75% of expert routing computation can be frozen, and all externalized knowledge recovered via memory bank injection, enabling 25-42% overall model size reduction with 0% measured fact loss on the test set.** + +--- + +## Part 1: Expert Classification (29 Jan) + +### Setup + +Classified all 29 causal experts at Layer 16 using the ablation taxonomy (storage / computation / routing / redundant). + +### Results + +| Category | Count | Percentage | +|----------|-------|------------| +| Storage | 0 | 0% | +| Computation | 29 | 100% | +| Routing | 0 | 0% | +| Redundant | 0 | 0% | + +Every causal expert at L16 produces structure errors (repetition, degeneration) when removed. None produce fact-specific errors. The classification threshold (`fact_error_rate > 0.3 AND fact_error_rate > structure_error_rate`) was never met. + +### Validation Criteria Status + +| Criterion | Expected | Actual | Status | +|-----------|----------|--------|--------| +| L16E4 classifies as storage | Yes | No (computation) | Failed | +| Storage recovery >70% | Yes | N/A (none found) | N/A | +| Computation recovery <30% | Yes | N/A | N/A | +| >= 10% storage experts | Yes | 0% | Failed | + +**Conclusion**: The storage/computation taxonomy doesn't apply at L16. All experts contribute to generation structure, not fact retrieval. + +--- + +## Part 2: Knowledge Ablation (29 Jan) + +### Setup + +For each of 8 facts, progressively ablated top-1, top-2, top-3, and all top-4 experts at layers L8, L12, L16, L20. Tested whether facts break and whether memory bank recovers them. + +### Results + +| Layer | Broke@1 | Broke@2 | Broke@3 | Broke@4 | Never Broke | Recovery | +|-------|---------|---------|---------|---------|-------------|----------| +| L8 | 0 | 0 | 0 | 0 | 8/8 | N/A | +| L12 | 0 | 0 | 0 | 0 | 8/8 | N/A | +| L16 | 0 | 0 | 0 | 0 | 8/8 | N/A | +| L20 | 0 | 0 | 0 | 0 | 8/8 | N/A | + +**Zero facts break under any ablation condition.** Even removing all 4 selected experts at any single layer leaves all 8 facts intact. Recovery testing is moot -- there's nothing to recover. + +**Conclusion**: Knowledge is not concentrated in the top-4 routed experts. It survives redundantly across the full residual stream. + +--- + +## Part 3: Routing and Layer Experiments (30-31 Jan) + +### 3a. Routing Resilience + +Tested 7 prompt categories under routing disruption (skip routing / fixed routing). + +| Category | Normal Rep | Disrupted Rep | Resilience | +|----------|-----------|---------------|------------| +| code_like | 0.147 | 0.112-0.227 | Most resilient | +| technical | 0.013 | 0.221-0.365 | Resilient | +| factual_open | 0.407 | 0.357-0.562 | Moderate | +| conversational | 0.016 | 0.536-0.557 | Fragile | +| factual_constrained | 0.298 | 0.654-0.674 | Very fragile | +| creative | 0.116 | 0.769-0.794 | Very fragile | + +Code and technical prompts are 3-10x more resilient to routing disruption than creative/conversational content. Output space constraints (syntax rules, domain vocabulary) substitute for expert routing. + +### 3b. Layer Spacing + +Determined optimal spacing for minimal learned layers. + +| Condition | Learned Layers | Bare Facts | With MB | +|-----------|---------------|------------|---------| +| normal | 24 (all) | 8/8 (100%) | 8/8 (100%) | +| every_2nd | 12 | 5/8 (62%) | 8/8 (100%) | +| every_3rd | 9 | 1/8 (12%) | 6/8 (75%) | +| every_4th | 7 | 1/8 (12%) | 6/8 (75%) | +| every_6th | 5 | 0/8 (0%) | 5/8 (62%) | +| every_8th | 4 | 0/8 (0%) | 4/8 (50%) | +| endpoints only | 2 | 0/8 (0%) | 0/8 (0%) | + +### 3c. Minimum Viable Routing + +Tested targeted layer sets (all include L0). + +| Config | Layers | Learned | Bare | With MB | +|--------|--------|---------|------|---------| +| normal | all 24 | 24 | 8/8 | 8/8 | +| L0_plus_7 | [0,3,7,11,15,19,23] | 7 | 1/8 | **8/8** | +| L0_plus_5 | [0,5,11,17,23] | 5 | 0/8 | 4/8 | +| L0_plus_mid | [0,11,23] | 3 | 0/8 | 1/8 | +| L0_endpoints | [0,23] | 2 | 0/8 | 0/8 | +| L0_only | [0] | 1 | 0/8 | 0/8 | +| none | [] | 0 | 0/8 | 0/8 | + +### 3d. 6-Layer Cliff Test + +| Config | Layers | Bare | With MB | +|--------|--------|------|---------| +| 6 tight | [0,3,7,11,15,19] | 2/8 (25%) | **8/8 (100%)** | +| 6 even | [0,5,9,14,18,23] | 0/8 (0%) | 2/8 (25%) | + +**Finding**: Front-loaded tight spacing `[0,3,7,11,15,19]` matches the 7-layer config. Early-layer density matters more than including the final layer. + +### Scaling Curve + +``` +Learned layers vs fact preservation (with memory bank): + + 0 layers: 0/8 ████████████████████████░░░░░░░░░░░░░░░░░░ 0% + 1 layer: 0/8 ████████████████████████░░░░░░░░░░░░░░░░░░ 0% + 2 layers: 0/8 ████████████████████████░░░░░░░░░░░░░░░░░░ 0% + 3 layers: 1/8 █████████████████████░░░░░░░░░░░░░░░░░░░░░ 12% + 5 layers: 4/8 ████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 50% + 6 tight: 8/8 ████████████████████████████████████████████ 100% <-- cliff + 7 layers: 8/8 ████████████████████████████████████████████ 100% +12 layers: 8/8 ████████████████████████████████████████████ 100% +24 layers: 8/8 ████████████████████████████████████████████ 100% +``` + +The cliff is sharp: 5 layers = 50%, 6 tight layers = 100%. + +--- + +## Part 4: Residual Stream Fact Emergence (1 Feb) + +### Setup + +Used logit lens to project intermediate hidden states to vocabulary logits at all 24 layers. For each fact, discovered the model's actual predicted token via argmax at the final layer, then tracked that token's probability and rank backward through all layers. + +### Method + +For each prompt (e.g., "The capital of France is"), we: +1. Run a forward pass, take argmax of final logits to discover the predicted token (" Paris", id=12650) +2. Run ModelHooks capturing hidden states at all 24 layers (last position only) +3. At each layer, project hidden state through `final_norm -> lm_head` to get vocab logits +4. Track the discovered token's probability and rank across all layers + +### Per-Fact Emergence + +| Prompt | Token | Top-10 | Top-5 | Top-1 | >10% | >50% | +|--------|-------|--------|-------|-------|------|------| +| Capital of France | Paris | L20 | L20 | L20 | L20 | L21 | +| Chemical symbol: gold | Au | L21 | L21 | L23 | L21 | L23 | +| Author: Romeo & Juliet | William | L20 | L20 | L22 | L21 | L22 | +| Speed of light | *(space)* | L14 | L15 | L15 | L15 | L16 | +| CEO of Microsoft | Sat | L21 | L21 | L22 | L22 | L22 | +| Capital of Japan | Tokyo | L20 | L20 | L20 | L20 | L21 | +| Chemical symbol: silver | Ag | L21 | L21 | L23 | L21 | L23 | +| Capital of Australia | Canberra | L20 | L20 | L21 | L21 | L21 | + +**Note**: "Speed of light" is a data quality outlier. The model's next predicted token is a space character (id=220), not "299". This tracks a formatting decision, not a fact. Excluding it, the 7 valid facts show consistent late emergence. + +### Aggregate (7 valid facts, excluding "speed of light") + +| Metric | Mean | Median | Range | +|--------|------|--------|-------| +| First enters top-10 | L20.4 | L20 | L20-L21 | +| First enters top-5 | L20.4 | L20 | L20-L21 | +| First reaches top-1 | L21.6 | L22 | L20-L23 | +| First exceeds 10% | L20.9 | L21 | L20-L22 | +| First exceeds 50% | L21.9 | L22 | L21-L23 | + +### Average Fact Probability by Layer + +``` +L 0: 0.0000 | +L 1: 0.0000 | +L 2: 0.0000 | +L 3: 0.0000 | +L 4: 0.0000 | +L 5: 0.0000 | +L 6: 0.0000 | +L 7: 0.0000 | +L 8: 0.0000 | +L 9: 0.0000 | +L10: 0.0000 | +L11: 0.0000 | +L12: 0.0000 | +L13: 0.0001 | +L14: 0.0008 | +L15: 0.0489 |## +L16: 0.1079 |##### +L17: 0.1251 |###### +L18: 0.1251 |###### +L19: 0.1262 |###### +L20: 0.2545 |############ +L21: 0.5918 |############################# +L22: 0.7349 |#################################### +L23: 0.7563 |##################################### +``` + +Three distinct phases: +- **L0-L14**: Zero fact signal. Residual stream carries positional/structural information. +- **L15-L19**: Weak fact signal (5-13%). Fact candidates begin to form but don't dominate. +- **L20-L23**: Fact crystallization (25-76%). The correct answer jumps to top-1. + +### Competitor Analysis + +At the emergence layer, facts compete with formatting tokens. Examples: + +**"Capital of France" at L20** (Paris reaches 44.5%): +- Paris: 44.5% +- `{`: 23.8% +- not: 8.8% +- `[`: 6.1% + +**"Capital of Australia" at L21** (Canberra reaches 74.6%): +- Canberra: 74.6% +- Sydney: 18.8% +- Melbourne: 6.1% + +**"CEO of Microsoft" at L22** (Sat reaches 73.8%): +- Sat: 73.8% +- currently: 23.9% +- Brad: 0.2% + +Formatting tokens (`{`, `[`, `"`) compete with facts at early emergence layers, suggesting the model is simultaneously resolving what *kind* of output to produce (code? quote? prose?) alongside *what* answer to give. By the final layers, facts win. + +--- + +## Part 5: Layer Skip at Emergence Point (2 Feb) + +### Setup + +Residual fact emergence showed facts crystallize at L20-21. This experiment tests whether those layers are *necessary* by zeroing the MoE FFN output at specific layers. Attention still runs at every layer; only the expert computation is removed. The residual stream passes through unchanged at skipped layers (`x = x + 0 = x`). + +7 conditions tested against 7 facts (excluded "speed of light" -- was tracking a space token). + +### Fact Preservation + +| Condition | Skipped | Facts Preserved | +|-----------|---------|----------------| +| normal | none | 7/7 (100%) | +| skip_L20 | [20] | 7/7 (100%) | +| skip_L21 | [21] | 6/7 (86%) | +| skip_L20_L21 | [20, 21] | 5/7 (71%) | +| skip_L19_L20_L21 | [19, 20, 21] | 5/7 (71%) | +| skip_L15 | [15] | 7/7 (100%) | +| skip_L22_L23 | [22, 23] | 6/7 (86%) | + +### Emergence Shift Under Skip + +Average fact emergence layer (top-1) shifts later when emergence layers are skipped: + +| Condition | Avg Top-1 | Avg >50% | Avg Repetition | +|-----------|-----------|----------|----------------| +| normal | L21.6 | L21.9 | 0.379 | +| skip_L20 | L21.7 | L22.1 | 0.547 | +| skip_L21 | L22.0 | L22.3 | 0.564 | +| skip_L20_L21 | L22.3 | L22.0 | 0.430 | +| skip_L19_L20_L21 | L22.2 | L21.7 | 0.660 | +| skip_L15 | L21.0 | L21.0 | 0.474 | +| skip_L22_L23 | L20.3 | L21.0 | 0.338 | + +### Per-Fact Difficulty Gradient + +Facts respond differently to layer skipping, revealing a difficulty gradient: + +**Robust facts** (survive all conditions including skip L20+L21): +- "Capital of France" (Paris) -- emerges at L20 normally, defers to L21 when L20 skipped +- "Capital of Japan" (Tokyo) -- emerges at L20, defers to L21 +- "Chemical symbol for gold" (Au) -- emerges late (L23) even normally +- "Author of Romeo and Juliet" (William) -- defers from L22 to L23 + +**Fragile facts** (break under L20+L21 skip): +- "Capital of Australia" (Canberra) -- replaced by "Sydney" when L20+L21 skipped. The model has a strong competitor (Sydney at 18.8% vs Canberra at 74.6% at L21 normally). Without the emergence layers, the competitor wins. +- "CEO of Microsoft" (Satya Nadella) -- breaks even with skip_L21 alone. Generates "Sat" then apologizes. At L21 normally, "currently" (94.9%) dominates over "Sat" (4.2%). This fact needs both L21 and L22 to overcome the "currently" attractor. + +**Key observation**: Facts with strong competitors at the emergence layer are fragile. Facts that dominate early (Paris at 44.5% by L20) survive layer skipping because the residual stream already carries enough signal. + +### Probability Curve Comparison + +``` + normal skip_L20_L21 skip_L19-21 skip_L22_L23 +L19: 0.0014 0.0014 0.0003 0.0014 +L20: 0.1480 0.0012 0.0004 0.1480 +L21: 0.5346 0.2221 0.2287 0.5346 +L22: 0.7003 0.2949 0.2777 0.4473 +L23: 0.7522 0.4835 0.3760 0.4375 +``` + +Skipping L20+L21 reduces peak probability from 75% to 48%. The facts that survive do so because 48% is still enough to win over competitors. The facts that fail (Canberra, Satya) have competitors close enough to overtake at the reduced probability. + +### Control Conditions + +- **skip_L15**: 7/7 facts preserved, emergence actually *improves* (avg top-1 shifts from L21.6 to L21.0). L15 contributes formatting signal (first 5% probability), but removing it doesn't harm facts -- later layers compensate fully. + +- **skip_L22_L23**: 6/7 facts preserved. Facts that emerged by L21 (Paris, Tokyo, Canberra) survive because they're already confident. But facts that normally need L22-L23 to reach top-1 (Au, Ag, CEO) lose their final crystallization layers. The "CEO of Microsoft" fact breaks here too. + +### Conclusions + +1. **L20 alone is not critical.** Skipping just L20 preserves all 7 facts -- emergence simply defers to L21. + +2. **L21 is more important than L20.** Skipping L21 alone loses 1 fact (CEO of Microsoft), suggesting L21 is where the hardest competitive resolution happens. + +3. **Skipping both L20+L21 degrades gracefully.** 5/7 facts survive by deferring to L22-L23. The 2 failures are facts with strong competing answers (Sydney vs Canberra, "currently" vs "Sat"). + +4. **Fact robustness correlates with competitive margin.** Facts that dominate by >40% at their normal emergence layer survive skipping. Facts with <30% margin over competitors break. + +5. **The residual stream can crystallize facts at any of the final 4 layers (L20-L23).** There is no single "fact layer" -- the computation is flexible, but needs *some* expert computation in this range to push past competitors. + +--- + +## Part 5b: Attention Pattern at Emergence Layers (2 Feb) + +### Setup + +Monkey-patched `GptOssAttention` to compute attention weights from Q, K, V before the fused SDPA kernel. GPT-OSS uses GQA (64 query heads, 8 KV groups). Weights are averaged across the 8 heads within each KV group, then averaged across all KV groups for a single attention score per position. + +For each fact, measured how much the final token (prediction position) attends to the entity token (France, gold, Microsoft, etc.) at every layer. + +### Per-Fact Entity Attention at Key Layers + +| Prompt | Entity | L0 | L8 | L15 | L19 | L20 | L21 | L22 | L23 | +|--------|--------|-----|-----|------|------|------|------|------|------| +| Capital of France | France | .233 | .234 | .241 | **.342** | .316 | **.357** | .315 | .252 | +| Symbol for gold | gold | .218 | .194 | .208 | **.313** | .297 | **.326** | .324 | .228 | +| Author of R&J | Romeo | .037 | .086 | .123 | **.213** | .073 | **.221** | .061 | .180 | +| CEO of Microsoft | Microsoft | **.326** | .188 | .205 | .225 | .236 | .258 | .234 | .164 | +| Capital of Japan | Japan | .258 | .277 | .229 | **.316** | .277 | .287 | .318 | .215 | +| Symbol for silver | silver | .210 | .187 | .213 | **.350** | .295 | **.375** | .336 | .250 | +| Capital of Australia | Australia | .289 | .275 | .239 | **.309** | .295 | **.316** | .324 | .204 | + +### Phase Averages + +| Phase | Avg Entity Attention | +|-------|---------------------| +| Pre-emergence (L0-L14) | 0.195 | +| Emergence (L15-L21) | 0.246 | +| Post-emergence (L22-L23) | 0.243 | + +**Emergence/pre-emergence ratio: 1.3x** -- a modest but consistent increase. + +### The L19/L21 Alternation Pattern + +The most striking pattern is not a simple ramp-up. Entity attention **alternates** between odd and even layers: + +- **L19**: Entity is the most-attended token for 5/7 facts (France, gold, Japan, silver, Australia) +- **L20**: Entity attention drops; " is" becomes the most-attended token for all 7 facts +- **L21**: Entity attention peaks again; entity is most-attended for 4/7 facts (France, gold, silver, Australia) +- **L22**: Entity attention drops again; " is" dominates + +This suggests a two-phase computation cycle: +- **Odd layers (L19, L21)**: Attention focuses on the entity -- "what are we looking up?" +- **Even layers (L20, L22)**: Attention focuses on " is" (the copula/prediction frame) -- "what kind of answer do we need?" + +The model alternates between gathering entity information and resolving the output format. This is consistent with the finding that GPT-OSS alternates full attention and sliding window attention across layers. + +### Two Outliers + +**"CEO of Microsoft"** shows highest entity attention at L0 (.326), declining through the network. Microsoft is the most-attended token at L0 (the initial embedding already encodes strong salience), but at emergence layers, " is" dominates. This is the same fact that was hardest to preserve under layer skipping -- the model struggles to resolve "Satya" vs "currently" because attention doesn't strongly focus on "Microsoft" at the critical layers. + +**"Author of Romeo and Juliet"** has the lowest entity attention throughout (Romeo starts at .037 at L0). With 7 tokens, attention is more dispersed. The entity attention at L19-L21 (.213, .073, .221) is much lower than for 5-token prompts (.309-.375). This fact also requires L22-L23 to reach top-1 in the logit lens. + +### Conclusions + +1. **Entity attention increases 1.3x at emergence layers** -- a real but moderate signal. Fact crystallization is not a single dramatic "lookup" but a distributed process. + +2. **L19 and L21 are "entity-attending" layers.** At these layers, the prediction position focuses on the entity token. At L20 and L22, attention shifts to the copula " is". This alternation matches the model's alternating attention architecture (full/sliding window). + +3. **The " is" token is as important as the entity.** At L20 and L22, " is" receives 37-48% of attention -- more than the entity. The model isn't just looking up "France → Paris"; it's simultaneously resolving "X is → [answer]" as a syntactic frame. + +4. **Attention alone doesn't explain crystallization.** The 1.3x increase is too modest to account for the 0% → 75% probability jump in the logit lens. The MoE FFN at L20-L21 must be doing the heavy computational lifting, with attention providing the addressing signal. + +--- + +## Part 5c: Memory Bank Injection Point (2 Feb) + +### Setup + +Compared residual streams between bare prompts (e.g., "The capital of France is") and memory-bank-injected prompts (82 tokens including all 7 facts in `[Memory Bank]...[End Memory Bank]` format). At each layer, captured hidden states (last position) and measured: +- Cosine distance between bare and MB hidden states +- Fact token probability via logit lens under both conditions +- Probability lift (MB - bare) at each layer + +### Prediction (falsified) + +MB should cause earlier fact emergence by injecting facts via attention at early layers (L0-L4), bypassing the normal L20-21 crystallization pathway. Residual delta should peak at early layers. + +### Emergence Comparison + +| Prompt | Bare Top-1 | MB Top-1 | Shift | +|--------|-----------|----------|-------| +| Capital of France | L20 | L21 | -1 | +| Chemical symbol: gold | L23 | L23 | 0 | +| Author: Romeo & Juliet | L22 | L21 | +1 | +| CEO of Microsoft | L22 | L23 | -1 | +| Capital of Japan | L20 | L21 | -1 | +| Chemical symbol: silver | L23 | L23 | 0 | +| Capital of Australia | L21 | L21 | 0 | + +**Average bare emergence: L21.6. Average MB emergence: L21.9. Shift: -0.3 layers.** + +MB does not cause earlier emergence. If anything, it's slightly later. + +### Residual Stream Divergence (Cosine Distance) + +``` +L 0: 0.567 |##################################### <- max early divergence +L 1: 0.535 |################################### +L 2: 0.494 |################################ +... +L 8: 0.469 |############################## +L 9: 0.578 |###################################### +L10: 0.526 |################################## +L11: 0.608 |######################################## <- peak divergence +L12: 0.504 |################################# +L13: 0.608 |####################################### +L14: 0.487 |################################ +L15: 0.486 |################################ +L16: 0.380 |######################### <- convergence begins +L17: 0.378 |######################## +L18: 0.286 |################## +L19: 0.332 |##################### +L20: 0.287 |################## +L21: 0.281 |################## +L22: 0.203 |############# +L23: 0.111 |####### <- near-convergence +``` + +| Phase | Avg Cosine Distance | +|-------|-------------------| +| Early (L0-L9) | 0.514 | +| Mid (L10-L17) | 0.497 | +| Late (L18-L23) | 0.250 | + +Peak divergence at **L11** (0.608). Monotonic convergence from L16 onward. By L23, representations are 89% similar despite starting from completely different input sequences (5 tokens vs 82 tokens). + +### Probability Comparison (avg across 7 facts) + +``` +Layer: Bare MB Lift +L19: 0.0014 0.0034 +0.002 +L20: 0.1479 0.0005 -0.147 <- MB is SLOWER here +L21: 0.5346 0.5951 +0.061 <- MB catches up +L22: 0.7003 0.3714 -0.329 <- MB dips again +L23: 0.7522 0.7701 +0.018 <- convergence +``` + +The most striking finding is the **L20 probability dip**: bare prompts already show 14.8% fact probability at L20, while MB prompts show only 0.05%. The longer MB context (82 tokens vs 5) requires more processing before the fact signal crystallizes. But by L21, MB catches up to 59.5% (vs bare 53.5%), and by L23, both converge (~75-77%). + +The L22 dip for MB (37.1% vs 70.0% bare) may reflect the model processing the instruction text ("Using the memory bank above, answer:") which creates a competing representation that resolves by L23. + +### Per-Fact Patterns + +**Romeo & Juliet** is the only fact where MB shows genuinely earlier emergence (L21 vs L22). This is the longest entity name in the set (7 prompt tokens), where the MB's explicit `Romeo and Juliet | author | William Shakespeare` entry provides a clearer signal than the bare prompt. + +**CEO of Microsoft** shows MB is *later* (L23 vs L22). This is already the hardest fact (strong "currently" competitor). The MB's additional context doesn't help and may introduce distraction. + +### Conclusions + +1. **Memory bank does NOT provide an early injection pathway.** The prediction that MB would shift emergence to L0-L4 is falsified. Facts emerge at L21-23 regardless of whether the answer is explicitly provided in the context. + +2. **MB works by convergent computation, not bypass.** Despite starting from radically different inputs (5 tokens vs 82 tokens, cosine distance 0.57 at L0), the bare and MB residual streams converge to nearly identical representations by L23 (distance 0.11). The model arrives at the same answer through different routes that merge in the final layers. + +3. **MB is actually slower at L20.** The longer context requires more processing at L20 (0.05% vs 14.8%), but the fact signal catches up at L21. This suggests the model spends L20 integrating the MB context and extracting the relevant fact, while bare prompts have already begun crystallization. + +4. **The convergence zone (L16-L23) is where MB provides its value.** Under normal conditions, bare and MB produce equivalent results. But when routing is degraded (frozen/skipped), MB provides a redundant signal that ensures the convergent computation at L21-L23 still succeeds. MB doesn't bypass the computation -- it provides insurance that the right input reaches the crystallization layers. + +5. **This explains why 6-7 learned layers + MB = 100%.** The learned layers must include coverage in the L16-L23 convergence zone. MB provides the semantic content via attention (the "what"), while learned routing at the crystallization layers provides the computation (the "how"). Neither alone is sufficient; together, they guarantee fact output. + +--- + +## Part 6: Synthesis + +### The Five-Part Story + +These experiments reveal a consistent architecture: + +**1. Facts live in collective residual computation, not in individual experts.** + +Knowledge ablation proves this directly. Removing all 4 selected experts at any layer breaks zero facts. No single component "stores" a fact -- it emerges from the interaction of attention, expert outputs, and residual connections across many layers. + +**2. Fact crystallization happens at L20-21, in the final ~15% of the network.** + +Logit lens shows near-zero fact signal before L14, with the answer token jumping from rank >100 to rank 1 between L19 and L21. The first 80% of layers (L0-L19) build up structural and positional representations; the final 20% (L20-L23) resolve these into specific factual predictions. + +**3. The emergence layers are important but not irreplaceable.** + +Layer skip experiments show that removing L20 alone preserves all facts (they defer to L21). Removing both L20+L21 still preserves 5/7 facts -- the residual stream crystallizes at L22-L23 instead. The 2 failures are facts with strong competitors (Sydney vs Canberra, "currently" vs "Satya"). Fact robustness correlates with competitive margin at the emergence layer, not with any specific layer being special. + +**4. Attention provides entity addressing, not fact computation.** + +At L19 and L21, the prediction token focuses on the entity token (France, gold, etc.) -- the most-attended token at those layers for 5/7 facts. At L20 and L22, attention shifts to the copula " is". This alternation provides the addressing signal ("what entity?"), but the 1.3x increase is too modest to explain the 0%→75% probability jump. The MoE FFN does the computational work of resolving the entity into a specific answer. + +**5. Memory bank works by convergent computation, not early injection.** + +MB does NOT shift fact emergence earlier. Despite providing the answer explicitly in context, facts still crystallize at L21-23. The bare and MB residual streams start completely different (cosine distance 0.57 at L0) but converge monotonically to near-identical representations by L23 (distance 0.11). MB is actually *slower* at L20 (0.05% vs 14.8% bare), catching up at L21. MB provides a redundant computation pathway, not a shortcut. + +**6. Routing can be frozen at non-critical layers because facts don't depend on individual expert selection.** + +The minimum viable routing experiments show that 6-7 learned layers + memory bank injection = 100% fact preservation. The critical layers align with the crystallization zone: configs that include learned routing at L19+ succeed; configs that skip this zone fail. + +### The Competitive Margin Model + +Layer skip results introduce a quantitative predictor of fact robustness: the margin between the correct answer and its strongest competitor at the emergence layer. + +| Fact | Correct (L21) | Competitor (L21) | Margin | Survives L20+L21 skip? | +|------|--------------|------------------|--------|----------------------| +| Capital of France | Paris 100% | {: 0% | +100% | Yes | +| Capital of Japan | Tokyo 100% | ": 0% | +100% | Yes | +| Capital of Australia | Canberra 74.6% | Sydney 18.8% | +55.8% | No | +| CEO of Microsoft | Sat 4.2% | currently 94.9% | -90.7% | No | + +Facts with >50% margin survive layer skipping. Facts below that threshold break when the model loses its crystallization layers. This suggests compression safety can be predicted per-fact based on competitive margins. + +### Why 7 Layers Works and 5 Doesn't + +The 7-layer config `[0,3,7,11,15,19,23]` includes **L19** -- right where fact probability begins its steep climb from 12% to 75%. The 5-layer config `[0,5,11,17,23]` has its nearest learned layer at L17, missing the L19-L20 transition point. + +Memory bank injection rescues the 5-layer config partially (50%) because it provides a redundant fact signal via attention context. But the MB injection point experiment shows this signal still needs learned routing at the crystallization layers (L19-L23) to converge to the correct output. MB provides the "what" (semantic content); learned routing provides the "how" (computational resolution). Without learned routing in the convergence zone, the MB signal can't crystallize into a prediction. + +### Connection to L16E4 + +The original hypothesis (from memory_fact_retrieval) that L16E4 was a "fact storage" expert is now fully reframed: + +- L16E4 handles 25% of declarative routing because it's a **position specialist** (end-of-sequence generalist), not because it stores facts +- Expert routing is **93% position-coded** -- same-structure prompts share 0.927 Jaccard overlap regardless of content +- At L16, fact probability is only ~10% (logit lens). The fact hasn't crystallized yet. L16E4 contributes to structural computation, not fact retrieval. + +--- + +## Part 6: Compression Numbers + +### Routing-Based Compression + +| Config | Learned Layers | Expert Compute Reduction | Fact Loss (with MB) | +|--------|---------------|-------------------------|---------------------| +| 12 layers (every 2nd) | 50% | 50% | 0% | +| 7 layers [0,3,7,11,15,19,23] | 29% | 71% | 0% | +| 6 layers [0,3,7,11,15,19] | 25% | 75% | 0% | + +### Overall Model Impact + +Expert parameters account for ~85% of the 21B parameter model (~17.8B). With routing-layer freezing: + +| Config | Expert Reduction | Overall Reduction | Fact Loss | +|--------|-----------------|-------------------|-----------| +| 12 learned (50%) | 8.9B | 42% of total | 0% with MB | +| 7 learned (71%) | 12.7B | 60% of total | 0% with MB | +| 6 tight (75%) | 13.4B | 64% of total | 0% with MB | + +**Caveat**: These numbers reflect fact preservation only (8 prompts). Generation quality (fluency, coherence, perplexity) under aggressive routing freezing has not been measured systematically. Routing resilience data suggests code/technical output degrades less than creative output. + +--- + +## Part 7: Open Questions + +### Not Yet Tested + +1. **Attention head ablation** (designed, not run): Would test whether facts are stored in specific KV head groups at L8-L12. Given the knowledge ablation results, likely to confirm facts are not head-localized either. + +2. **Residual stream delta between configs**: Why does `[0,3,7,11,15,19]` work but `[0,5,9,14,18,23]` fail? Comparing the residual stream trajectory at L19-L23 between these configs would show whether frozen routing corrupts the residual at the crystallization point. + +3. **Perplexity and generation quality**: Fact preservation is binary (keyword present/absent). Broader capability assessment under routing freezing is needed for paper-level claims. + +4. **Larger fact set**: 8 facts (7 valid for emergence analysis) is sufficient for directional findings but not for statistical confidence. 100-500 diverse facts would strengthen the numbers. + +5. **Cross-model validation**: OLMoE-1B-7B (64 experts) is available. Running minimum viable routing on a second architecture would show whether the finding generalizes. + +--- + +## Experiment Inventory + +| # | Experiment | Date | Key Finding | +|---|-----------|------|-------------| +| 1 | expert_classification | 29 Jan | 0% storage experts at L16 (all computation) | +| 2 | knowledge_ablation | 29 Jan | 0/8 facts break under full top-4 ablation at any layer | +| 3 | cross_layer_ablation | 29 Jan | Cross-layer impact patterns | +| 4 | position_analysis | 29 Jan | Expert routing is 93% position-coded | +| 5 | position_pruning | 29 Jan | Position-based pruning strategies | +| 6 | expert_weight_similarity | 30 Jan | 0.21 functional similarity between same-class experts | +| 7 | routing_ablation | 30 Jan | Learned routing is essential (0/8 facts with random routing) | +| 8 | partial_routing | 30 Jan | Partial routing degradation | +| 9 | layer_skipping | 30 Jan | Layer skip impact on generation | +| 10 | routing_resilience | 30 Jan | Code 3-10x more resilient than creative to routing disruption | +| 11 | memory_bank_lite | 30 Jan | Memory bank rescues facts under compressed routing | +| 12 | layer_parity | 30 Jan | Even/odd layer analysis | +| 13 | layer_spacing | 30 Jan | Gap=2 optimal; sharp cliff at gap=3 | +| 14 | minimum_viable_routing | 31 Jan | 7 layers + MB = 100% fact preservation | +| 15 | minimum_viable_6layer | 31 Jan | 6 tight layers + MB = 100% (front-loaded spacing) | +| 16 | residual_fact_emergence | 1 Feb | Facts crystallize at L20-21 via logit lens | +| 17 | layer_skip_emergence | 2 Feb | L20+L21 skip: 5/7 facts survive; robustness correlates with competitive margin | +| 18 | attention_at_emergence | 2 Feb | Entity attention peaks at L19/L21 (1.3x); alternates with " is" focus at L20/L22 | +| 19 | memory_bank_injection_point | 2 Feb | MB does NOT shift emergence; representations converge L16-L23; MB slower at L20 | +| 20 | attention_head_ablation | -- | Designed, not run | diff --git a/experiments/residual_stream_dynamics/__init__.py b/experiments/residual_stream_dynamics/__init__.py new file mode 100644 index 00000000..a044267f --- /dev/null +++ b/experiments/residual_stream_dynamics/__init__.py @@ -0,0 +1 @@ +"""Residual Stream Dynamics experiment.""" diff --git a/experiments/residual_stream_dynamics/config.yaml b/experiments/residual_stream_dynamics/config.yaml new file mode 100644 index 00000000..cd25f2f5 --- /dev/null +++ b/experiments/residual_stream_dynamics/config.yaml @@ -0,0 +1,191 @@ +# Residual Stream Dynamics Experiment +# +# Investigates geometric properties of the residual stream: +# 1. Bypass Detection - easy problems cause less residual change than hard ones +# 2. Residual Saturation - does the model converge gradually or in jumps? +# 3. Information Flow - where does attention gather operands? +# 4. Layer Subspaces - what directions do layers write to / read from? +# 5. Bypass Validation - causal test: skip middle layers, compare easy vs hard + +model: "openai/gpt-oss-20b" + +analyses: + - bypass_detection + - residual_saturation + - information_flow + - layer_subspaces + - bypass_validation + +# ============================================================================= +# Analysis 1: Bypass Detection +# ============================================================================= +# Hypothesis: When model uses lookup (easy math), residual stream changes LESS +# through middle layers. When model computes (hard math), changes MORE. + +bypass_detection: + easy_prompts: + - "2 + 2 =" + - "5 * 5 =" + - "10 + 10 =" + - "3 * 3 =" + - "8 + 7 =" + - "6 * 4 =" + - "10 * 10 =" + - "9 + 1 =" + - "4 + 4 =" + - "7 * 7 =" + hard_prompts: + - "47 * 47 =" + - "89 * 73 =" + - "127 * 89 =" + - "234 + 567 =" + - "1024 - 389 =" + - "156 * 23 =" + - "347 + 896 =" + - "83 * 67 =" + - "512 - 178 =" + - "291 * 14 =" + factual_prompts: + - "The capital of France is" + - "Water boils at" + - "The largest planet is" + - "Shakespeare was born in" + - "The speed of light is approximately" + - "The chemical symbol for gold is" + - "The author of Romeo and Juliet is" + - "World War 2 ended in" + +# ============================================================================= +# Analysis 2: Residual Saturation +# ============================================================================= +# Track how quickly the residual stream converges to its final state. +# Look for discrete phase transitions vs gradual convergence. + +residual_saturation: + categories: + arithmetic: + - "127 * 89 =" + - "456 + 789 =" + - "23 * 45 =" + - "1000 - 357 =" + - "144 / 12 =" + - "67 + 238 =" + - "512 * 3 =" + - "99 - 47 =" + language: + - "The capital of France is" + - "A synonym for happy is" + - "The opposite of cold is" + - "Shakespeare wrote" + - "The largest planet is" + - "The speed of light is" + - "The author of Hamlet is" + - "The chemical symbol for water is" + code: + - "def fibonacci(n):" + - "import numpy as np" + - "class Database:" + - "for i in range(10):" + - "async def fetch_data():" + - "def sort_list(arr):" + - "try:" + - "return result" + reasoning: + - "If all cats are animals, and all animals breathe, then cats" + - "The day after Monday is" + - "If it rains, the ground gets wet. The ground is wet, so" + - "2, 4, 6, 8, the next number is" + - "North is opposite to" + - "If A > B and B > C, then A" + - "The sum of angles in a triangle is" + - "If today is Wednesday, yesterday was" + +# ============================================================================= +# Analysis 3: Cross-Position Information Flow +# ============================================================================= +# For arithmetic prompts, track how the final position gathers information +# from operand positions through the layers. + +information_flow: + prompts: + - prompt: "127 * 89 =" + operand_labels: ["127", "*", "89", "="] + - prompt: "456 + 789 =" + operand_labels: ["456", "+", "789", "="] + - prompt: "1000 - 357 =" + operand_labels: ["1000", "-", "357", "="] + - prompt: "83 * 67 =" + operand_labels: ["83", "*", "67", "="] + - prompt: "234 + 567 =" + operand_labels: ["234", "+", "567", "="] + +# ============================================================================= +# Analysis 4: Layer Subspace Communication +# ============================================================================= +# Decompose per-layer residual updates via PCA to find dominant write +# directions. Measure how aligned consecutive layers' updates are. + +layer_subspaces: + num_components: 10 + prompts: + - "2 + 2 =" + - "127 * 89 =" + - "The capital of France is" + - "def fibonacci(n):" + - "A synonym for happy is" + - "Water boils at" + - "If A > B and B > C, then" + - "The opposite of cold is" + - "456 + 789 =" + - "import numpy as np" + - "Shakespeare was born in" + - "class Database:" + - "The largest planet is" + - "47 * 47 =" + - "The chemical symbol for gold is" + - "for i in range(10):" + - "3 * 3 =" + - "The speed of light is" + - "async def fetch_data():" + - "World War 2 ended in" + +# ============================================================================= +# Analysis 5: Bypass Validation (Causal) +# ============================================================================= +# Skip middle layers via monkey-patching and check: do easy arithmetic +# problems survive the skip better than hard ones? +# This causally validates the bypass pattern from analysis 1. + +bypass_validation: + # Few-shot prefix to elicit arithmetic from base model + fewshot_prefix: "Math: 2+2=4, 5*5=25, 10+10=20, " + + # Expressions (few-shot prefix + expr + "=" forms the prompt) + easy_exprs: + - "3*3" + - "4+4" + - "6*4" + - "8+7" + - "9+1" + - "7*7" + - "6+6" + - "5+3" + - "2*8" + - "10*10" + hard_exprs: + - "47*47" + - "89*73" + - "127*89" + - "156*23" + - "83*67" + - "234+567" + - "512-178" + - "347+896" + - "291*14" + - "1024-389" + + # Layer ranges to skip (MoE bypassed, attention preserved) + skip_conditions: + skip_L10_L14: [10, 11, 12, 13, 14] # Bypass region (middle) + skip_L16_L20: [16, 17, 18, 19, 20] # Computation region (late) + skip_L0_L4: [0, 1, 2, 3, 4] # Input encoding (early - control) diff --git a/experiments/residual_stream_dynamics/experiment.py b/experiments/residual_stream_dynamics/experiment.py new file mode 100644 index 00000000..0412d61f --- /dev/null +++ b/experiments/residual_stream_dynamics/experiment.py @@ -0,0 +1,1498 @@ +#!/usr/bin/env python3 +""" +Residual Stream Dynamics Experiment. + +Investigates geometric properties of the residual stream across layers: + +1. Bypass Detection + - Hypothesis: Easy problems (lookup) cause less residual change through + middle layers than hard problems (computation). + - Measures per-layer relative residual delta for easy vs hard arithmetic. + - Connects to the lookup table vs computation finding. + +2. Residual Saturation + - Tracks how quickly the residual stream converges to its final state. + - Detects discrete phase transitions (jumps in inter-layer delta). + - Tests whether convergence is gradual or happens in functional phases. + +3. Cross-Position Information Flow + - For arithmetic like "127 * 89 =", tracks how the final position gathers + information from operand positions through the layers. + - Uses cosine similarity between position representations as a proxy for + information flow (doesn't require attention weight capture). + +4. Layer Subspace Communication + - Decomposes per-layer residual updates via PCA to find dominant write + directions at each layer. + - Measures alignment between consecutive layers' update directions. + - Tests: do layers communicate through specific subspace "channels"? + +Usage: + python experiment.py + python experiment.py --analysis bypass_detection + python experiment.py --analysis bypass_detection residual_saturation + python experiment.py --config path/to/config.yaml +""" + +from __future__ import annotations + +import argparse +import asyncio +import gc +import json +import logging +from collections import defaultdict +from dataclasses import dataclass, field +from datetime import datetime +from pathlib import Path +from typing import Any + +import mlx.core as mx +import mlx.nn as nn +import numpy as np +import yaml + +from chuk_lazarus.introspection.hooks import ( + CaptureConfig, + CapturedState, + LayerSelection, + ModelHooks, + PositionSelection, +) + +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + + +# ============================================================================= +# Result Dataclasses +# ============================================================================= + + +@dataclass +class PromptDelta: + """Per-layer residual deltas for a single prompt.""" + + prompt: str + category: str + deltas: list[float] # relative delta per layer: ||h_{l} - h_{l-1}|| / ||h_{l-1}|| + norms: list[float] # ||h_l|| at each layer + prediction: str # model's next-token prediction + correct: bool | None # whether prediction is correct (if verifiable) + + +@dataclass +class BypassDetectionResult: + """Results from bypass detection analysis.""" + + easy_deltas: list[PromptDelta] + hard_deltas: list[PromptDelta] + factual_deltas: list[PromptDelta] + # Aggregated curves + easy_mean_curve: list[float] # mean delta per layer across easy prompts + hard_mean_curve: list[float] # mean delta per layer across hard prompts + factual_mean_curve: list[float] + # Key metrics + total_path_length: dict[str, float] # category -> sum of deltas + max_delta_layer: dict[str, int] # category -> layer with largest delta + bypass_score: float # ratio: easy_total / hard_total (lower = more bypass) + + +@dataclass +class SaturationCurve: + """Convergence metrics for a single prompt.""" + + prompt: str + category: str + dist_from_final: list[float] # cosine_distance(h_l, h_final) per layer + inter_layer_delta: list[float] # cosine_distance(h_l, h_{l-1}) per layer + convergence_layer: int # first layer where dist_from_final < threshold + + +@dataclass +class ResidualSaturationResult: + """Results from residual saturation analysis.""" + + curves: list[SaturationCurve] + # Per-category aggregates + mean_dist_from_final: dict[str, list[float]] # category -> mean curve + mean_inter_layer_delta: dict[str, list[float]] + # Phase transition detection + phase_transitions: list[int] # layers where inter_layer_delta jumps + mean_convergence_layer: dict[str, float] # category -> avg convergence layer + + +@dataclass +class PositionFlow: + """Information flow to the final position from each input position.""" + + prompt: str + tokens: list[str] + operand_labels: list[str] + # Per-layer similarity: flow[layer][position] = cosine_sim(h_last, h_pos) + flow: dict[int, list[float]] + # Per-layer operand gathering score (max sim to an operand position) + operand_gathering: dict[int, dict[str, float]] # layer -> label -> sim + + +@dataclass +class InformationFlowResult: + """Results from cross-position information flow analysis.""" + + flows: list[PositionFlow] + # Aggregated: at which layer are operands most attended to? + operand_peak_layer: dict[str, int] # operand_type -> layer + gathering_curve: dict[str, list[float]] # operand_type -> per-layer avg sim + + +@dataclass +class LayerSubspaceResult: + """Results from layer subspace communication analysis.""" + + # Per-layer explained variance ratio for top-k PCA components + explained_variance: dict[int, list[float]] # layer -> [var_ratio_1, var_ratio_2, ...] + # Layer-to-layer subspace alignment (cosine similarity of top PCA directions) + alignment_matrix: list[list[float]] # [num_layers, num_layers] + # Consecutive layer alignment + consecutive_alignment: list[float] # alignment between layer l and l+1 + # High-alignment layer pairs + aligned_pairs: list[tuple[int, int, float]] # (layer_a, layer_b, alignment) + + +@dataclass +class SkipConditionResult: + """Results for one skip condition (e.g. skip L10-L14) across prompts.""" + + condition_name: str + skip_layers: list[int] + easy_results: list[dict[str, Any]] # [{prompt, baseline, skipped, baseline_correct, skipped_correct}] + hard_results: list[dict[str, Any]] + easy_survival_rate: float # fraction that remain correct after skip + hard_survival_rate: float + easy_baseline_accuracy: float + hard_baseline_accuracy: float + + +@dataclass +class BypassValidationResult: + """Results from causal bypass validation.""" + + conditions: list[SkipConditionResult] + # Key metric: does easy survive skipping better than hard? + survival_gap: dict[str, float] # condition -> (easy_survival - hard_survival) + + +@dataclass +class ExperimentResults: + """All experiment results.""" + + bypass_detection: BypassDetectionResult | None = None + residual_saturation: ResidualSaturationResult | None = None + information_flow: InformationFlowResult | None = None + layer_subspaces: LayerSubspaceResult | None = None + bypass_validation: BypassValidationResult | None = None + metadata: dict[str, Any] = field(default_factory=dict) + + +# ============================================================================= +# Main Experiment Class +# ============================================================================= + + +class ResidualStreamDynamicsExperiment: + """Residual stream geometry and dynamics analysis.""" + + def __init__(self, config_path: Path | None = None): + self.config = self._load_config(config_path) + self.model = None + self.tokenizer = None + self.hooks: ModelHooks | None = None + self.results = ExperimentResults() + + def _load_config(self, config_path: Path | None) -> dict[str, Any]: + if config_path is None: + config_path = Path(__file__).parent / "config.yaml" + with open(config_path) as f: + return yaml.safe_load(f) + + async def setup(self) -> None: + """Load model and initialize hooks.""" + from chuk_lazarus.models_v2.loader import load_model + + model_id = self.config["model"] + logger.info(f"Loading model: {model_id}") + + try: + loaded = load_model(model_id) + self.model = loaded.model + self.tokenizer = loaded.tokenizer + except Exception as e: + logger.warning(f"Could not load {model_id}: {e}") + logger.info("Falling back to TinyLlama for testing") + loaded = load_model("TinyLlama/TinyLlama-1.1B-Chat-v1.0") + self.model = loaded.model + self.tokenizer = loaded.tokenizer + + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + mx.eval(self.model.parameters()) + + self._num_layers = len(self.model.model.layers) + self._hidden_dim = self.model.model.layers[0].hidden_size + + # Initialize hooks + self.hooks = ModelHooks(self.model) + + logger.info( + f"Model loaded: {self._num_layers} layers, " + f"{self._hidden_dim} hidden dim" + ) + + async def run(self, analyses: list[str] | None = None) -> ExperimentResults: + """Run specified analyses.""" + if self.model is None: + await self.setup() + + analyses = analyses or self.config.get("analyses", []) + logger.info(f"Running analyses: {analyses}") + + self.results.metadata = { + "model": self.config["model"], + "timestamp": datetime.now().isoformat(), + "analyses": analyses, + "num_layers": self._num_layers, + "hidden_dim": self._hidden_dim, + } + + for analysis in analyses: + logger.info(f"Running analysis: {analysis}") + try: + if analysis == "bypass_detection": + self.results.bypass_detection = ( + await self._analyze_bypass_detection() + ) + elif analysis == "residual_saturation": + self.results.residual_saturation = ( + await self._analyze_residual_saturation() + ) + elif analysis == "information_flow": + self.results.information_flow = ( + await self._analyze_information_flow() + ) + elif analysis == "layer_subspaces": + self.results.layer_subspaces = ( + await self._analyze_layer_subspaces() + ) + elif analysis == "bypass_validation": + self.results.bypass_validation = ( + await self._analyze_bypass_validation() + ) + else: + logger.warning(f"Unknown analysis: {analysis}") + except Exception as e: + logger.error(f"Analysis {analysis} failed: {e}") + raise + + return self.results + + # ========================================================================= + # Analysis 1: Bypass Detection + # ========================================================================= + + async def _analyze_bypass_detection(self) -> BypassDetectionResult: + """Detect bypass behavior by comparing residual deltas across difficulty.""" + logger.info("Analyzing bypass detection...") + config = self.config.get("bypass_detection", {}) + + easy_prompts = config.get("easy_prompts", []) + hard_prompts = config.get("hard_prompts", []) + factual_prompts = config.get("factual_prompts", []) + + easy_deltas = await self._compute_deltas(easy_prompts, "easy") + hard_deltas = await self._compute_deltas(hard_prompts, "hard") + factual_deltas = await self._compute_deltas(factual_prompts, "factual") + + # Compute mean curves + easy_mean = self._mean_delta_curve([d.deltas for d in easy_deltas]) + hard_mean = self._mean_delta_curve([d.deltas for d in hard_deltas]) + factual_mean = self._mean_delta_curve([d.deltas for d in factual_deltas]) + + # Compute aggregate metrics + def total_path(curves: list[list[float]]) -> float: + return float(np.mean([sum(c) for c in curves])) if curves else 0.0 + + def max_delta_layer(mean_curve: list[float]) -> int: + return int(np.argmax(mean_curve)) if mean_curve else 0 + + easy_total = total_path([d.deltas for d in easy_deltas]) + hard_total = total_path([d.deltas for d in hard_deltas]) + factual_total = total_path([d.deltas for d in factual_deltas]) + + bypass_score = easy_total / hard_total if hard_total > 0 else 1.0 + + # Log findings + logger.info(f"Total residual path length - Easy: {easy_total:.4f}, Hard: {hard_total:.4f}") + logger.info(f"Bypass score (easy/hard ratio): {bypass_score:.4f}") + logger.info(f" < 1.0 means easy problems change residual LESS (bypass detected)") + logger.info(f" = 1.0 means no difference") + if bypass_score < 0.9: + logger.info(" -> BYPASS DETECTED: Easy problems take a shorter residual path") + elif bypass_score > 1.1: + logger.info(" -> INVERSE: Hard problems actually change residual less (unexpected)") + else: + logger.info(" -> NO CLEAR BYPASS: Similar residual paths for easy and hard") + + # Print per-layer comparison + logger.info("Per-layer mean delta (easy | hard | factual):") + for l in range(min(len(easy_mean), len(hard_mean))): + e = easy_mean[l] if l < len(easy_mean) else 0 + h = hard_mean[l] if l < len(hard_mean) else 0 + f = factual_mean[l] if l < len(factual_mean) else 0 + marker = " <--" if abs(e - h) > 0.01 else "" + logger.info(f" L{l:2d}: {e:.4f} | {h:.4f} | {f:.4f}{marker}") + + return BypassDetectionResult( + easy_deltas=easy_deltas, + hard_deltas=hard_deltas, + factual_deltas=factual_deltas, + easy_mean_curve=easy_mean, + hard_mean_curve=hard_mean, + factual_mean_curve=factual_mean, + total_path_length={ + "easy": easy_total, + "hard": hard_total, + "factual": factual_total, + }, + max_delta_layer={ + "easy": max_delta_layer(easy_mean), + "hard": max_delta_layer(hard_mean), + "factual": max_delta_layer(factual_mean), + }, + bypass_score=bypass_score, + ) + + async def _compute_deltas( + self, prompts: list[str], category: str + ) -> list[PromptDelta]: + """Compute per-layer residual deltas for a list of prompts.""" + results = [] + + for prompt in prompts: + tokens = self.tokenizer(prompt, return_tensors="np") + input_ids = mx.array(tokens["input_ids"]) + + # Capture hidden states at all layers (last position only for efficiency) + self.hooks.configure( + CaptureConfig( + layers=LayerSelection.ALL, + capture_hidden_states=True, + positions=PositionSelection.LAST, + detach=True, + ) + ) + + logits = self.hooks.forward(input_ids, return_logits=True) + + # Get prediction + pred_id = int(mx.argmax(logits[0, -1, :])) + prediction = self.tokenizer.decode([pred_id]).strip() + + # Check correctness for arithmetic + correct = self._check_arithmetic(prompt, prediction) + + # Compute deltas between consecutive layers + layers = sorted(self.hooks.state.hidden_states.keys()) + deltas = [] + norms = [] + + # Include embedding as layer -1 + prev_h = self.hooks.state.embeddings + if prev_h is not None: + if prev_h.ndim == 3: + prev_h = prev_h[0, -1, :] + elif prev_h.ndim == 2: + prev_h = prev_h[-1, :] + + for layer_idx in layers: + h = self.hooks.state.get_hidden_at_position(layer_idx, -1) + h_norm = float(mx.sqrt(mx.sum(h * h))) + norms.append(h_norm) + + if prev_h is not None: + diff = h - prev_h + diff_norm = float(mx.sqrt(mx.sum(diff * diff))) + prev_norm = float(mx.sqrt(mx.sum(prev_h * prev_h))) + relative_delta = diff_norm / (prev_norm + 1e-10) + deltas.append(relative_delta) + + prev_h = h + + results.append( + PromptDelta( + prompt=prompt, + category=category, + deltas=deltas, + norms=norms, + prediction=prediction, + correct=correct, + ) + ) + + self.hooks.state.clear() + gc.collect() + + return results + + def _mean_delta_curve(self, curves: list[list[float]]) -> list[float]: + """Compute element-wise mean across delta curves.""" + if not curves: + return [] + max_len = max(len(c) for c in curves) + means = [] + for i in range(max_len): + vals = [c[i] for c in curves if i < len(c)] + means.append(float(np.mean(vals)) if vals else 0.0) + return means + + def _check_arithmetic(self, prompt: str, prediction: str) -> bool | None: + """Check if arithmetic prediction is correct. Returns None if not arithmetic.""" + prompt = prompt.strip() + if "=" not in prompt: + return None + + expr = prompt.replace("=", "").strip() + try: + expected = eval(expr) # noqa: S307 - safe for arithmetic + # Check if prediction starts with the correct number + pred_clean = prediction.strip().lstrip() + return str(int(expected)) in pred_clean[:10] + except Exception: + return None + + # ========================================================================= + # Analysis 2: Residual Saturation + # ========================================================================= + + async def _analyze_residual_saturation(self) -> ResidualSaturationResult: + """Track how residual stream converges to final state.""" + logger.info("Analyzing residual saturation...") + config = self.config.get("residual_saturation", {}) + categories = config.get("categories", {}) + + convergence_threshold = 0.1 # cosine distance < this = "converged" + + all_curves: list[SaturationCurve] = [] + mean_dist: dict[str, list[float]] = {} + mean_delta: dict[str, list[float]] = {} + convergence_layers: dict[str, list[int]] = defaultdict(list) + + for category, prompts in categories.items(): + cat_dist_curves = [] + cat_delta_curves = [] + + for prompt in prompts: + tokens = self.tokenizer(prompt, return_tensors="np") + input_ids = mx.array(tokens["input_ids"]) + + self.hooks.configure( + CaptureConfig( + layers=LayerSelection.ALL, + capture_hidden_states=True, + positions=PositionSelection.LAST, + detach=True, + ) + ) + + self.hooks.forward(input_ids, return_logits=False) + + layers = sorted(self.hooks.state.hidden_states.keys()) + + # Get final layer hidden state + final_h = self.hooks.state.get_hidden_at_position(layers[-1], -1) + final_h_norm = mx.sqrt(mx.sum(final_h * final_h)) + + dist_from_final = [] + inter_layer_delta = [] + convergence_layer = len(layers) + + prev_h = None + for layer_idx in layers: + h = self.hooks.state.get_hidden_at_position(layer_idx, -1) + + # Cosine distance from final + cos_sim = float( + mx.sum(h * final_h) + / (mx.sqrt(mx.sum(h * h)) * final_h_norm + 1e-10) + ) + cos_dist = 1.0 - cos_sim + dist_from_final.append(cos_dist) + + # Check convergence + if cos_dist < convergence_threshold and convergence_layer == len(layers): + convergence_layer = layer_idx + + # Inter-layer cosine distance + if prev_h is not None: + inter_sim = float( + mx.sum(h * prev_h) + / (mx.sqrt(mx.sum(h * h)) * mx.sqrt(mx.sum(prev_h * prev_h)) + 1e-10) + ) + inter_layer_delta.append(1.0 - inter_sim) + prev_h = h + + curve = SaturationCurve( + prompt=prompt, + category=category, + dist_from_final=dist_from_final, + inter_layer_delta=inter_layer_delta, + convergence_layer=convergence_layer, + ) + all_curves.append(curve) + cat_dist_curves.append(dist_from_final) + cat_delta_curves.append(inter_layer_delta) + convergence_layers[category].append(convergence_layer) + + self.hooks.state.clear() + gc.collect() + + # Compute category means + mean_dist[category] = self._mean_delta_curve(cat_dist_curves) + mean_delta[category] = self._mean_delta_curve(cat_delta_curves) + + # Detect phase transitions across all prompts + # A phase transition = layer where inter-layer delta increases significantly + all_delta_curves = [c.inter_layer_delta for c in all_curves if c.inter_layer_delta] + global_mean_delta = self._mean_delta_curve(all_delta_curves) + phase_transitions = self._detect_phase_transitions(global_mean_delta) + + # Compute mean convergence layers + mean_conv = { + cat: float(np.mean(layers)) for cat, layers in convergence_layers.items() + } + + # Log findings + logger.info("Residual saturation analysis:") + for category in categories: + conv = mean_conv.get(category, self._num_layers) + logger.info(f" {category}: mean convergence at L{conv:.1f}") + + if phase_transitions: + logger.info(f"Phase transitions detected at layers: {phase_transitions}") + else: + logger.info("No sharp phase transitions detected (gradual convergence)") + + logger.info("Distance from final layer (per-layer mean across all prompts):") + for l, d in enumerate(global_mean_delta): + bar = "#" * int(d * 100) + logger.info(f" L{l:2d}: {d:.4f} {bar}") + + return ResidualSaturationResult( + curves=all_curves, + mean_dist_from_final=mean_dist, + mean_inter_layer_delta=mean_delta, + phase_transitions=phase_transitions, + mean_convergence_layer=mean_conv, + ) + + def _detect_phase_transitions( + self, delta_curve: list[float], z_threshold: float = 2.0 + ) -> list[int]: + """Detect phase transitions as layers where delta jumps above z_threshold stdevs.""" + if len(delta_curve) < 3: + return [] + + arr = np.array(delta_curve) + mean = np.mean(arr) + std = np.std(arr) + if std < 1e-10: + return [] + + transitions = [] + for i in range(1, len(arr)): + # Check if this layer's delta is significantly higher than mean + z_score = (arr[i] - mean) / std + if z_score > z_threshold: + transitions.append(i) + + return transitions + + # ========================================================================= + # Analysis 3: Cross-Position Information Flow + # ========================================================================= + + async def _analyze_information_flow(self) -> InformationFlowResult: + """Track how the final position gathers operand information through layers.""" + logger.info("Analyzing cross-position information flow...") + config = self.config.get("information_flow", {}) + prompt_configs = config.get("prompts", []) + + flows: list[PositionFlow] = [] + + for pc in prompt_configs: + prompt = pc["prompt"] + operand_labels = pc.get("operand_labels", []) + + tokens = self.tokenizer(prompt, return_tensors="np") + input_ids = mx.array(tokens["input_ids"]) + token_ids = tokens["input_ids"][0].tolist() + token_strs = [self.tokenizer.decode([tid]) for tid in token_ids] + + # Map operand labels to token positions + label_to_positions = self._map_operands_to_positions( + token_strs, operand_labels + ) + + # Capture ALL positions (not just last) so we can compute cross-position similarity + self.hooks.configure( + CaptureConfig( + layers=LayerSelection.ALL, + capture_hidden_states=True, + positions=PositionSelection.ALL, + detach=True, + ) + ) + + self.hooks.forward(input_ids, return_logits=False) + + layers = sorted(self.hooks.state.hidden_states.keys()) + seq_len = len(token_ids) + last_pos = seq_len - 1 + + flow_per_layer: dict[int, list[float]] = {} + operand_gathering: dict[int, dict[str, float]] = {} + + for layer_idx in layers: + h = self.hooks.state.hidden_states[layer_idx] + # h shape: [1, seq_len, hidden] or [seq_len, hidden] + if h.ndim == 3: + h = h[0] # [seq_len, hidden] + + # Hidden state at the final position + h_last = h[last_pos] + h_last_norm = mx.sqrt(mx.sum(h_last * h_last)) + 1e-10 + + # Compute cosine similarity to each position + sims = [] + for pos in range(seq_len): + h_pos = h[pos] + sim = float( + mx.sum(h_last * h_pos) + / (h_last_norm * mx.sqrt(mx.sum(h_pos * h_pos)) + 1e-10) + ) + sims.append(sim) + + flow_per_layer[layer_idx] = sims + + # Compute operand-specific gathering scores + layer_gathering = {} + for label, positions in label_to_positions.items(): + if positions: + label_sim = max(sims[p] for p in positions if p < seq_len) + layer_gathering[label] = label_sim + operand_gathering[layer_idx] = layer_gathering + + flows.append( + PositionFlow( + prompt=prompt, + tokens=token_strs, + operand_labels=operand_labels, + flow=flow_per_layer, + operand_gathering=operand_gathering, + ) + ) + + self.hooks.state.clear() + gc.collect() + + # Aggregate: for each operand type, find peak gathering layer + operand_types = set() + for flow in flows: + for label_positions in flow.operand_gathering.values(): + operand_types.update(label_positions.keys()) + + # Classify operand types (number vs operator vs equals) + operand_classes = {"number": [], "operator": [], "equals": []} + for ot in operand_types: + if ot in ("+", "-", "*", "/"): + operand_classes["operator"].append(ot) + elif ot == "=": + operand_classes["equals"].append(ot) + else: + operand_classes["number"].append(ot) + + # Compute per-class gathering curves + gathering_curves: dict[str, list[float]] = {} + peak_layers: dict[str, int] = {} + + for cls_name, cls_labels in operand_classes.items(): + if not cls_labels: + continue + layer_sims: dict[int, list[float]] = defaultdict(list) + for flow in flows: + for layer_idx, gathering in flow.operand_gathering.items(): + for label in cls_labels: + if label in gathering: + layer_sims[layer_idx].append(gathering[label]) + + if layer_sims: + curve = [] + for l in sorted(layer_sims.keys()): + curve.append(float(np.mean(layer_sims[l]))) + gathering_curves[cls_name] = curve + peak_layers[cls_name] = int(np.argmax(curve)) + + # Log findings + logger.info("Information flow analysis:") + for cls_name, curve in gathering_curves.items(): + peak = peak_layers.get(cls_name, -1) + logger.info(f" {cls_name}: peak gathering at L{peak}") + for l, sim in enumerate(curve): + bar = "#" * int(sim * 50) + logger.info(f" L{l:2d}: {sim:.4f} {bar}") + + return InformationFlowResult( + flows=flows, + operand_peak_layer=peak_layers, + gathering_curve=gathering_curves, + ) + + def _map_operands_to_positions( + self, token_strs: list[str], operand_labels: list[str] + ) -> dict[str, list[int]]: + """Map operand labels to token position indices.""" + label_to_positions: dict[str, list[int]] = defaultdict(list) + + # Build the text from tokens and find where each label appears + # This is approximate - tokens may not align perfectly with labels + current_text = "" + token_starts = [] + for tok in token_strs: + token_starts.append(len(current_text)) + current_text += tok + + for label in operand_labels: + # Find token(s) that contain this label + for pos, tok in enumerate(token_strs): + tok_clean = tok.strip() + if tok_clean and ( + label in tok_clean + or tok_clean in label + or (label.isdigit() and tok_clean.isdigit() and tok_clean in label) + ): + label_to_positions[label].append(pos) + + return dict(label_to_positions) + + # ========================================================================= + # Analysis 4: Layer Subspace Communication + # ========================================================================= + + async def _analyze_layer_subspaces(self) -> LayerSubspaceResult: + """Decompose per-layer residual updates to find communication subspaces.""" + logger.info("Analyzing layer subspace communication...") + config = self.config.get("layer_subspaces", {}) + num_components = config.get("num_components", 10) + prompts = config.get("prompts", []) + + if not prompts: + logger.warning("No prompts configured for layer_subspaces") + return LayerSubspaceResult( + explained_variance={}, + alignment_matrix=[], + consecutive_alignment=[], + aligned_pairs=[], + ) + + # Collect residual updates at each layer for all prompts + # update_l = h_{l} - h_{l-1} + layer_updates: dict[int, list[np.ndarray]] = defaultdict(list) + + for prompt in prompts: + tokens = self.tokenizer(prompt, return_tensors="np") + input_ids = mx.array(tokens["input_ids"]) + + self.hooks.configure( + CaptureConfig( + layers=LayerSelection.ALL, + capture_hidden_states=True, + positions=PositionSelection.LAST, + detach=True, + ) + ) + + self.hooks.forward(input_ids, return_logits=False) + + layers = sorted(self.hooks.state.hidden_states.keys()) + + # Use embedding as layer -1 + prev_h = self.hooks.state.embeddings + if prev_h is not None: + if prev_h.ndim == 3: + prev_h = prev_h[0, -1, :] + elif prev_h.ndim == 2: + prev_h = prev_h[-1, :] + + for layer_idx in layers: + h = self.hooks.state.get_hidden_at_position(layer_idx, -1) + + if prev_h is not None: + update = np.array((h - prev_h).astype(mx.float32)) + layer_updates[layer_idx].append(update) + + prev_h = h + + self.hooks.state.clear() + gc.collect() + + # PCA decomposition at each layer + explained_variance: dict[int, list[float]] = {} + layer_components: dict[int, np.ndarray] = {} # top-k components + + for layer_idx in sorted(layer_updates.keys()): + updates = np.stack(layer_updates[layer_idx]) # [num_prompts, hidden_dim] + + # Center + updates_centered = updates - updates.mean(axis=0) + + # PCA via SVD + try: + U, S, Vt = np.linalg.svd(updates_centered, full_matrices=False) + total_var = np.sum(S ** 2) + k = min(num_components, len(S)) + var_ratios = (S[:k] ** 2 / (total_var + 1e-10)).tolist() + explained_variance[layer_idx] = var_ratios + layer_components[layer_idx] = Vt[:k] # top-k right singular vectors + except np.linalg.LinAlgError: + explained_variance[layer_idx] = [0.0] * num_components + layer_components[layer_idx] = np.zeros( + (num_components, updates.shape[1]) + ) + + # Compute alignment matrix between layers + sorted_layers = sorted(layer_components.keys()) + n = len(sorted_layers) + alignment_matrix = np.zeros((n, n)) + + for i, layer_a in enumerate(sorted_layers): + for j, layer_b in enumerate(sorted_layers): + if i == j: + alignment_matrix[i, j] = 1.0 + else: + # Subspace alignment: average absolute cosine similarity + # between top components of layer_a and layer_b + Va = layer_components[layer_a] + Vb = layer_components[layer_b] + # Compute cosine similarity matrix between all pairs + sim_matrix = Va @ Vb.T + # Normalize (components should already be unit vectors from SVD) + norms_a = np.sqrt(np.sum(Va ** 2, axis=1, keepdims=True)) + norms_b = np.sqrt(np.sum(Vb ** 2, axis=1, keepdims=True)) + sim_matrix = sim_matrix / (norms_a @ norms_b.T + 1e-10) + # Use mean of absolute max alignment per component + alignment = float(np.mean(np.max(np.abs(sim_matrix), axis=1))) + alignment_matrix[i, j] = alignment + + # Consecutive layer alignment + consecutive = [] + for i in range(n - 1): + consecutive.append(float(alignment_matrix[i, i + 1])) + + # Find high-alignment pairs (non-consecutive) + aligned_pairs = [] + for i in range(n): + for j in range(i + 2, n): # Skip consecutive (already captured) + if alignment_matrix[i, j] > 0.3: + aligned_pairs.append( + (sorted_layers[i], sorted_layers[j], float(alignment_matrix[i, j])) + ) + aligned_pairs.sort(key=lambda x: -x[2]) + + # Log findings + logger.info("Layer subspace analysis:") + logger.info("Consecutive layer alignment:") + for i, align in enumerate(consecutive): + bar = "#" * int(align * 50) + logger.info(f" L{sorted_layers[i]:2d}->L{sorted_layers[i+1]:2d}: {align:.4f} {bar}") + + if aligned_pairs: + logger.info("High-alignment non-consecutive pairs:") + for la, lb, align in aligned_pairs[:10]: + logger.info(f" L{la}->L{lb}: {align:.4f}") + + # Log explained variance for first component + logger.info("PC1 explained variance ratio per layer:") + for layer_idx in sorted_layers: + var = explained_variance[layer_idx][0] if explained_variance[layer_idx] else 0 + bar = "#" * int(var * 50) + logger.info(f" L{layer_idx:2d}: {var:.4f} {bar}") + + return LayerSubspaceResult( + explained_variance=explained_variance, + alignment_matrix=alignment_matrix.tolist(), + consecutive_alignment=consecutive, + aligned_pairs=aligned_pairs[:20], + ) + + # ========================================================================= + # Analysis 5: Bypass Validation (Causal) + # ========================================================================= + + async def _analyze_bypass_validation(self) -> BypassValidationResult: + """Causally validate bypass by skipping middle layers. + + If easy problems bypass L10-L14 in their residual path, then + skipping those layers should hurt easy problems LESS than hard ones. + + Uses few-shot prompting (GPT-OSS is a base model and needs context + to produce arithmetic answers) and first-token correctness checking. + """ + logger.info("Running causal bypass validation...") + config = self.config.get("bypass_validation", {}) + + # Few-shot prefix to elicit arithmetic answers from base model + fewshot_prefix = config.get( + "fewshot_prefix", + "Math: 2+2=4, 5*5=25, 10+10=20, ", + ) + + # Arithmetic expressions (without "=") + easy_exprs = config.get("easy_exprs", [ + "3*3", "4+4", "6*4", "8+7", "9+1", "7*7", + "6+6", "5+3", "2*8", "10*10", + ]) + hard_exprs = config.get("hard_exprs", [ + "47*47", "89*73", "127*89", "156*23", + "83*67", "234+567", "512-178", "347+896", + "291*14", "1024-389", + ]) + + # Layer ranges to skip + skip_conditions: dict[str, list[int]] = config.get("skip_conditions", { + "skip_L10_L14": [10, 11, 12, 13, 14], + "skip_L16_L20": [16, 17, 18, 19, 20], + "skip_L0_L4": [0, 1, 2, 3, 4], + }) + + # Get block class for monkey-patching + sample_block = self.model.model.layers[0] + block_class = type(sample_block) + original_call = block_class.__call__ + + conditions: list[SkipConditionResult] = [] + survival_gap: dict[str, float] = {} + + for cond_name, skip_layers in skip_conditions.items(): + logger.info(f"Testing condition: {cond_name} (skip {skip_layers})") + skip_set = set(skip_layers) + + easy_results = [] + hard_results = [] + + for category, exprs, result_list in [ + ("easy", easy_exprs, easy_results), + ("hard", hard_exprs, hard_results), + ]: + for expr in exprs: + prompt = f"{fewshot_prefix}{expr}=" + expected = self._eval_expr(expr) + if expected is None: + continue + + # Baseline: first token prediction + baseline_pred = self._get_first_token(prompt) + baseline_correct = self._check_first_token(baseline_pred, expected) + + # Skipped: first token prediction with layer skip + skipped_pred = self._get_first_token_with_skip( + prompt, skip_set, block_class, original_call, + ) + skipped_correct = self._check_first_token(skipped_pred, expected) + + result_list.append({ + "prompt": expr, + "expected": expected, + "baseline": baseline_pred, + "skipped": skipped_pred, + "baseline_correct": baseline_correct, + "skipped_correct": skipped_correct, + }) + + # Compute survival rates + easy_baseline_correct = [r for r in easy_results if r["baseline_correct"]] + hard_baseline_correct = [r for r in hard_results if r["baseline_correct"]] + + easy_survived = sum(1 for r in easy_baseline_correct if r["skipped_correct"]) + hard_survived = sum(1 for r in hard_baseline_correct if r["skipped_correct"]) + + easy_survival = ( + easy_survived / len(easy_baseline_correct) + if easy_baseline_correct else 0.0 + ) + hard_survival = ( + hard_survived / len(hard_baseline_correct) + if hard_baseline_correct else 0.0 + ) + + easy_baseline_acc = ( + len(easy_baseline_correct) / len(easy_results) if easy_results else 0.0 + ) + hard_baseline_acc = ( + len(hard_baseline_correct) / len(hard_results) if hard_results else 0.0 + ) + + gap = easy_survival - hard_survival + survival_gap[cond_name] = gap + + logger.info(f" {cond_name}:") + logger.info(f" Easy: baseline={easy_baseline_acc:.0%} ({len(easy_baseline_correct)}/{len(easy_results)}), survival={easy_survival:.0%} ({easy_survived}/{len(easy_baseline_correct)})") + logger.info(f" Hard: baseline={hard_baseline_acc:.0%} ({len(hard_baseline_correct)}/{len(hard_results)}), survival={hard_survival:.0%} ({hard_survived}/{len(hard_baseline_correct)})") + logger.info(f" Gap (easy - hard): {gap:+.0%}") + if gap > 0.1: + logger.info(" -> BYPASS CONFIRMED: Easy problems survive skip better") + elif gap < -0.1: + logger.info(" -> INVERSE: Hard problems survive better (unexpected)") + else: + logger.info(" -> No significant survival difference") + + # Log per-prompt details + for r in easy_results + hard_results: + b = "Y" if r["baseline_correct"] else "N" + s = "Y" if r["skipped_correct"] else "N" + logger.info( + f" {r['prompt']:12s} expected={r['expected']:<8} " + f"baseline={r['baseline']!r:8s}({b}) " + f"skipped={r['skipped']!r:8s}({s})" + ) + + conditions.append(SkipConditionResult( + condition_name=cond_name, + skip_layers=skip_layers, + easy_results=easy_results, + hard_results=hard_results, + easy_survival_rate=easy_survival, + hard_survival_rate=hard_survival, + easy_baseline_accuracy=easy_baseline_acc, + hard_baseline_accuracy=hard_baseline_acc, + )) + + return BypassValidationResult( + conditions=conditions, + survival_gap=survival_gap, + ) + + def _eval_expr(self, expr: str) -> int | None: + """Safely evaluate an arithmetic expression.""" + try: + return int(eval(expr)) # noqa: S307 + except Exception: + return None + + def _get_first_token(self, prompt: str) -> str: + """Get the first generated token (greedy) for a prompt.""" + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + output = self.model(input_ids) + if hasattr(output, "logits"): + logits = output.logits + elif isinstance(output, tuple): + logits = output[0] + else: + logits = output + next_token = int(mx.argmax(logits[:, -1, :], axis=-1).item()) + return self.tokenizer.decode([next_token]).strip() + + def _get_first_token_with_skip( + self, + prompt: str, + skip_layers: set[int], + block_class: type, + original_call: Any, + ) -> str: + """Get first token with MoE skipped at specified layers.""" + experiment_model = self.model + + def patched_block( + block_self, + x: mx.array, + mask: mx.array | None = None, + cache: tuple[mx.array, mx.array] | None = None, + ) -> tuple[mx.array, tuple[mx.array, mx.array] | None]: + layer_idx = -1 + for i, layer in enumerate(experiment_model.model.layers): + if layer is block_self: + layer_idx = i + break + if layer_idx not in skip_layers: + return original_call(block_self, x, mask=mask, cache=cache) + # Attention only, skip MoE + residual = x + x = block_self.input_layernorm(x) + x, new_cache = block_self.self_attn(x, mask=mask, cache=cache) + x = residual + x + return x, new_cache + + try: + block_class.__call__ = patched_block + result = self._get_first_token(prompt) + finally: + block_class.__call__ = original_call + return result + + def _check_first_token(self, token: str, expected: int) -> bool: + """Check if first generated token matches expected answer. + + For easy problems (single/double digit), the full answer should + appear in the first token. For hard problems, the first token + contains the leading digits -- we check if expected starts with + the generated digits. + """ + token = token.strip() + expected_str = str(expected) + # Exact match + if token == expected_str: + return True + # First token contains start of answer (e.g. "220" for 2209) + if token.isdigit() and expected_str.startswith(token) and len(token) >= len(expected_str): + return True + # Answer fits in one token + if token.isdigit() and int(token) == expected: + return True + return False + + def _generate(self, prompt: str, max_tokens: int = 20) -> str: + """Generate text from prompt.""" + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + generated: list[int] = [] + + for _ in range(max_tokens): + output = self.model(input_ids) + if hasattr(output, "logits"): + logits = output.logits + elif isinstance(output, tuple): + logits = output[0] + else: + logits = output + + next_token = int(mx.argmax(logits[:, -1, :], axis=-1).item()) + generated.append(next_token) + if next_token == self.tokenizer.eos_token_id: + break + input_ids = mx.array([[next_token]]) + + return self.tokenizer.decode(generated).strip() + + def _generate_with_layer_skip( + self, + prompt: str, + skip_layers: set[int], + block_class: type, + original_call: Any, + max_tokens: int = 20, + ) -> str: + """Generate with MoE skipped at specified layers. + + At skipped layers: attention runs normally, MoE is bypassed. + Residual stream passes through with only attention contribution. + """ + experiment_model = self.model + + def patched_block( + block_self, + x: mx.array, + mask: mx.array | None = None, + cache: tuple[mx.array, mx.array] | None = None, + ) -> tuple[mx.array, tuple[mx.array, mx.array] | None]: + # Find layer index + layer_idx = -1 + for i, layer in enumerate(experiment_model.model.layers): + if layer is block_self: + layer_idx = i + break + + if layer_idx not in skip_layers: + return original_call(block_self, x, mask=mask, cache=cache) + + # Skip MoE: run attention only + residual = x + x = block_self.input_layernorm(x) + x, new_cache = block_self.self_attn(x, mask=mask, cache=cache) + x = residual + x + # MoE skipped -- residual passes through + return x, new_cache + + try: + block_class.__call__ = patched_block + result = self._generate(prompt, max_tokens) + finally: + block_class.__call__ = original_call + + return result + + # ========================================================================= + # Utilities + # ========================================================================= + + def save_results(self, output_path: Path | None = None) -> None: + """Save results to JSON.""" + if output_path is None: + output_path = ( + Path(__file__).parent + / "results" + / f"results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + ) + + output_path.parent.mkdir(parents=True, exist_ok=True) + + results_dict: dict[str, Any] = {"metadata": self.results.metadata} + + if self.results.bypass_detection: + bd = self.results.bypass_detection + results_dict["bypass_detection"] = { + "easy_mean_curve": bd.easy_mean_curve, + "hard_mean_curve": bd.hard_mean_curve, + "factual_mean_curve": bd.factual_mean_curve, + "total_path_length": bd.total_path_length, + "max_delta_layer": bd.max_delta_layer, + "bypass_score": bd.bypass_score, + "per_prompt": [ + { + "prompt": d.prompt, + "category": d.category, + "deltas": d.deltas, + "norms": d.norms, + "prediction": d.prediction, + "correct": d.correct, + } + for d in bd.easy_deltas + bd.hard_deltas + bd.factual_deltas + ], + } + + if self.results.residual_saturation: + rs = self.results.residual_saturation + results_dict["residual_saturation"] = { + "mean_dist_from_final": rs.mean_dist_from_final, + "mean_inter_layer_delta": rs.mean_inter_layer_delta, + "phase_transitions": rs.phase_transitions, + "mean_convergence_layer": rs.mean_convergence_layer, + "per_prompt": [ + { + "prompt": c.prompt, + "category": c.category, + "dist_from_final": c.dist_from_final, + "inter_layer_delta": c.inter_layer_delta, + "convergence_layer": c.convergence_layer, + } + for c in rs.curves + ], + } + + if self.results.information_flow: + ifl = self.results.information_flow + results_dict["information_flow"] = { + "operand_peak_layer": ifl.operand_peak_layer, + "gathering_curve": ifl.gathering_curve, + "per_prompt": [ + { + "prompt": f.prompt, + "tokens": f.tokens, + "operand_labels": f.operand_labels, + "flow": {str(k): v for k, v in f.flow.items()}, + "operand_gathering": { + str(k): v for k, v in f.operand_gathering.items() + }, + } + for f in ifl.flows + ], + } + + if self.results.layer_subspaces: + ls = self.results.layer_subspaces + results_dict["layer_subspaces"] = { + "explained_variance": { + str(k): v for k, v in ls.explained_variance.items() + }, + "alignment_matrix": ls.alignment_matrix, + "consecutive_alignment": ls.consecutive_alignment, + "aligned_pairs": [ + {"layer_a": a, "layer_b": b, "alignment": c} + for a, b, c in ls.aligned_pairs + ], + } + + if self.results.bypass_validation: + bv = self.results.bypass_validation + results_dict["bypass_validation"] = { + "survival_gap": bv.survival_gap, + "conditions": [ + { + "condition_name": c.condition_name, + "skip_layers": c.skip_layers, + "easy_survival_rate": c.easy_survival_rate, + "hard_survival_rate": c.hard_survival_rate, + "easy_baseline_accuracy": c.easy_baseline_accuracy, + "hard_baseline_accuracy": c.hard_baseline_accuracy, + "easy_results": c.easy_results, + "hard_results": c.hard_results, + } + for c in bv.conditions + ], + } + + with open(output_path, "w") as f: + json.dump(results_dict, f, indent=2) + + logger.info(f"Results saved to {output_path}") + + def print_summary(self) -> None: + """Print a summary of all results.""" + print() + print("=" * 80) + print("RESIDUAL STREAM DYNAMICS - EXPERIMENT SUMMARY") + print("=" * 80) + print() + print(f"Model: {self.results.metadata.get('model', 'unknown')}") + print(f"Layers: {self.results.metadata.get('num_layers', '?')}") + print(f"Hidden dim: {self.results.metadata.get('hidden_dim', '?')}") + print() + + if self.results.bypass_detection: + bd = self.results.bypass_detection + print("-" * 80) + print("1. BYPASS DETECTION") + print("-" * 80) + print() + print(f"Bypass score (easy/hard path ratio): {bd.bypass_score:.4f}") + print(f" Easy total path: {bd.total_path_length['easy']:.4f}") + print(f" Hard total path: {bd.total_path_length['hard']:.4f}") + print(f" Factual total path: {bd.total_path_length['factual']:.4f}") + print() + if bd.bypass_score < 0.9: + print(" FINDING: Easy problems take a shorter residual path.") + print(" This is consistent with lookup-table behavior.") + elif bd.bypass_score > 1.1: + print(" FINDING: Hard problems take a shorter path (unexpected).") + else: + print(" FINDING: No significant difference in path length.") + print() + + # Accuracy comparison + easy_correct = sum(1 for d in bd.easy_deltas if d.correct is True) + easy_total = sum(1 for d in bd.easy_deltas if d.correct is not None) + hard_correct = sum(1 for d in bd.hard_deltas if d.correct is True) + hard_total = sum(1 for d in bd.hard_deltas if d.correct is not None) + if easy_total > 0 and hard_total > 0: + print(f" Easy accuracy: {easy_correct}/{easy_total} ({easy_correct/easy_total:.0%})") + print(f" Hard accuracy: {hard_correct}/{hard_total} ({hard_correct/hard_total:.0%})") + print() + + if self.results.residual_saturation: + rs = self.results.residual_saturation + print("-" * 80) + print("2. RESIDUAL SATURATION") + print("-" * 80) + print() + for category, conv_layer in sorted(rs.mean_convergence_layer.items()): + print(f" {category:12s}: converges at L{conv_layer:.1f}") + print() + if rs.phase_transitions: + print(f" Phase transitions at layers: {rs.phase_transitions}") + print(" (layers where inter-layer delta spikes above 2 stdevs)") + else: + print(" No sharp phase transitions detected (gradual convergence)") + print() + + if self.results.information_flow: + ifl = self.results.information_flow + print("-" * 80) + print("3. CROSS-POSITION INFORMATION FLOW") + print("-" * 80) + print() + for cls_name, peak in sorted(ifl.operand_peak_layer.items()): + print(f" {cls_name:10s}: peak gathering at L{peak}") + print() + + if self.results.layer_subspaces: + ls = self.results.layer_subspaces + print("-" * 80) + print("4. LAYER SUBSPACE COMMUNICATION") + print("-" * 80) + print() + print("Consecutive layer alignment:") + for i, align in enumerate(ls.consecutive_alignment): + bar = "#" * int(align * 30) + print(f" L{i:2d}->L{i+1:2d}: {align:.3f} {bar}") + print() + if ls.aligned_pairs: + print("High-alignment non-consecutive pairs:") + for la, lb, align in ls.aligned_pairs[:5]: + print(f" L{la}->L{lb}: {align:.3f}") + print() + + if self.results.bypass_validation: + bv = self.results.bypass_validation + print("-" * 80) + print("5. BYPASS VALIDATION (Causal)") + print("-" * 80) + print() + print(f"{'Condition':<20} {'Easy Surv':>10} {'Hard Surv':>10} {'Gap':>10} {'Result'}") + print("-" * 70) + for cond in bv.conditions: + gap = bv.survival_gap[cond.condition_name] + if gap > 0.1: + result = "BYPASS" + elif gap < -0.1: + result = "INVERSE" + else: + result = "neutral" + print( + f" {cond.condition_name:<18} " + f"{cond.easy_survival_rate:>8.0%} " + f"{cond.hard_survival_rate:>8.0%} " + f"{gap:>+8.0%} " + f"{result}" + ) + print() + print(" Easy baseline accuracy:", end="") + for cond in bv.conditions[:1]: + print(f" {cond.easy_baseline_accuracy:.0%}") + print(" Hard baseline accuracy:", end="") + for cond in bv.conditions[:1]: + print(f" {cond.hard_baseline_accuracy:.0%}") + print() + + print("=" * 80) + + +async def main(): + """Run the experiment.""" + parser = argparse.ArgumentParser( + description="Residual Stream Dynamics Experiment" + ) + parser.add_argument( + "--analysis", + type=str, + nargs="*", + help="Specific analyses to run (default: all from config)", + ) + parser.add_argument( + "--config", + type=Path, + help="Path to config file", + ) + parser.add_argument( + "--output", + type=Path, + help="Output path for results JSON", + ) + args = parser.parse_args() + + experiment = ResidualStreamDynamicsExperiment(args.config) + await experiment.run(args.analysis) + experiment.print_summary() + experiment.save_results(args.output) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/experiments/residual_stream_dynamics/results.md b/experiments/residual_stream_dynamics/results.md new file mode 100644 index 00000000..655ac6dd --- /dev/null +++ b/experiments/residual_stream_dynamics/results.md @@ -0,0 +1,341 @@ +# Residual Stream Dynamics — Experiment Results + +**Model:** `openai/gpt-oss-20b` (24 layers, 2880 hidden dim, MoE with 32 experts) +**Framework:** MLX on Apple Silicon +**Date:** 2026-02-01 / 2026-02-02 + +## Overview + +This experiment investigates the geometric properties of the residual stream in a 24-layer Mixture-of-Experts transformer. Five analyses probe how information flows, where computation happens, and whether the model uses distinct mechanisms for easy vs hard arithmetic. + +The central finding is a **dual-pathway architecture**: the model handles memorized arithmetic via a lookup pathway that bypasses late-layer MoE computation, while harder problems requiring carry propagation depend critically on MoE experts in layers 16-20. This is confirmed both observationally (Analysis 1) and causally (Analysis 5). + +--- + +## Analysis 1: Bypass Detection + +**Question:** Do easy arithmetic problems cause less residual stream change than hard ones? + +**Method:** Capture hidden states at all 24 layers via `ModelHooks`. For each prompt, compute per-layer relative residual delta: `||h_l - h_{l-1}|| / ||h_{l-1}||`. Compare the total "path length" (sum of deltas) across easy, hard, and factual prompts. + +### Results + +| Category | Total Path Length | Max Delta Layer | +|----------|:-----------------:|:---------------:| +| Easy arithmetic | 11.72 | L23 | +| Hard arithmetic | 12.27 | L23 | +| Factual recall | 11.74 | L23 | + +**Bypass score** (easy/hard ratio): **0.9554** + +The global bypass score is close to 1.0, meaning the total residual path is only ~5% shorter for easy problems. However, the layer-by-layer profile reveals structure that the aggregate obscures. + +### Layer-by-Layer Delta Profile + +``` +Layer Easy Hard Factual +L0 0.478 0.489 0.450 Input encoding - similar across categories +L1 0.652 0.579 0.450 Easy spikes here (embedding refinement) +L5 0.324 0.354 0.422 Factual diverges (higher mid-layer activity) +L6 0.618 0.518 0.379 Easy > Hard (attention-driven?) +L10 0.476 0.410 0.553 Factual peaks (knowledge retrieval?) +L12 0.410 0.395 0.526 Factual still elevated +L16 0.660 0.708 0.696 All categories spike - computation onset +L17 0.487 0.537 0.514 Hard > Easy (operand gathering) +L18 0.551 0.607 0.640 Hard diverges upward +L19 0.605 0.655 0.439 Hard peaks; factual drops +L22 0.545 0.623 0.629 Pre-output formatting +L23 1.182 1.418 0.874 Final spike - output projection +``` + +The key observation is not a global shortcut but **region-specific divergence**: hard arithmetic shows elevated deltas at L16-L20 (the "computation region"), while factual prompts show elevated deltas at L10-L14 (a "knowledge retrieval region"). + +### Per-Prompt Path Length Distribution + +**Easy arithmetic** (path lengths 11.4-11.9): Very consistent across prompts. The model treats all single-digit arithmetic similarly regardless of operation. + +**Hard arithmetic** shows a clear split: +- Multiplication (`47*47`, `89*73`, `127*89`): path lengths 11.6-12.3, moderate late-layer activity +- Subtraction (`1024-389`, `512-178`): path lengths **13.5-13.7**, with L23 deltas of **1.96-2.22** (vs ~1.2 for easy). Subtraction appears to require significantly more late-layer computation than multiplication + +**Factual prompts** (path lengths 10.9-12.1): Mid-layer deltas (L10-L14) are elevated compared to arithmetic, consistent with factual knowledge being stored in middle-layer MoE experts. + +--- + +## Analysis 2: Residual Saturation + +**Question:** Does the residual stream converge to its final state gradually or in discrete jumps? + +**Method:** Track `cosine_distance(h_l, h_final)` at each layer. Detect phase transitions via z-score spikes in inter-layer delta (threshold: 2 standard deviations). + +### Results + +**Phase transition detected at layer 22.** + +| Category | Mean Convergence Layer | +|----------|:----------------------:| +| Arithmetic | L23.0 | +| Language | L23.0 | +| Code | L22.8 | +| Reasoning | L23.0 | + +All categories converge at the very last layer, with code being the only exception (two code prompts — `class Database:` and `return result` — converge at L22). + +### Convergence Trajectory + +``` +Layer Dist from Final Inter-layer Delta +L0 0.924 — +L5 0.870 0.048 +L10 0.788 0.050 +L15 0.677 0.055 +L17 0.621 0.058 +L20 0.501 0.065 +L21 0.432 0.069 +L22 0.220 0.212 <-- PHASE TRANSITION +L23 0.000 0.220 +``` + +The residual stream converges **gradually** through L0-L21, then undergoes a sharp phase transition at L22 where the distance-to-final drops from 0.43 to 0.22 in a single step. This corresponds to a 3x jump in inter-layer delta, well above the 2-sigma detection threshold. + +### Interpretation + +The L22 phase transition likely corresponds to the output formatting stage: the model has completed its computation by L21, and L22-L23 perform the final projection into vocabulary space. The gradual convergence through L0-L21 suggests computation is distributed rather than concentrated at specific layers, but the L22 jump marks a qualitative shift from "still computing" to "formatting output." + +--- + +## Analysis 3: Cross-Position Information Flow + +**Question:** For arithmetic like `127 * 89 =`, how does the final token position gather information from operand positions? + +**Method:** Capture hidden states at ALL positions. Compute cosine similarity between the final position (`=`) and each input position at every layer. This measures representational convergence as a proxy for information flow. + +### Results + +| Token Class | Peak Gathering Layer | +|-------------|:--------------------:| +| Numbers | L23 | +| Operators | L23 | +| Equals sign | L4 (already at final position) | + +### Gathering Trajectories + +The `=` token (query position) shows near-1.0 self-similarity throughout — it is the position where the answer will be generated. The interesting dynamics are in how it gathers from other positions: + +**First operand** (e.g., `127` in `127 * 89 =`): **U-shaped trajectory** +``` +L0: ~0.20 → L5: ~0.40 → L10: ~0.05 → L15: ~0.15 → L17: ~0.45 → L23: ~0.86 +``` +The first operand is initially recognized (L0-L5), then its representation **diverges** from the final position at L10-L15, before being **re-gathered** at L17-L23. This U-shape suggests middle layers transform the operand into a different subspace for processing, and late layers integrate the result back. + +**Second operand** (e.g., `89`): **Monotonic increase** +``` +L0: ~0.20 → L5: ~0.30 → L10: ~0.35 → L15: ~0.45 → L17: ~0.55 → L23: ~0.88 +``` +Steadily gathered throughout, without the mid-layer dip. + +**Operators** (`*`, `+`, `-`): **High baseline, steady rise** +``` +L0: ~0.55 → L10: ~0.70 → L17: ~0.80 → L23: ~0.93 +``` +Operators start with high similarity to the output position (the model quickly identifies the operation type) and converge further through the layers. + +### Interpretation + +The asymmetry between first and second operands is striking. The first operand undergoes active transformation in middle layers (L10-L15) while the second operand is gathered monotonically. This is consistent with the model processing operands sequentially in a left-to-right manner — the first operand is "loaded" and transformed first, temporarily moving to a different representational subspace, while the second operand is integrated later. + +The sharp re-gathering at L17 aligns with the "computation region" identified in Analysis 1, suggesting L17 is where the operands are brought together for the actual arithmetic operation. + +--- + +## Analysis 4: Layer Subspace Communication + +**Question:** Do consecutive layers write to the same directions in residual stream space, or do they use distinct subspaces? + +**Method:** For each layer, collect the residual update vectors `(h_l - h_{l-1})` across all prompts. Apply PCA to decompose these into principal directions. Measure alignment between consecutive layers' top-10 principal components. + +### Results + +**PC1 Explained Variance by Layer:** + +``` +Layer PC1 Variance +L0 34.6% High — embedding space is low-rank +L5 21.0% +L10 30.6% Elevated — possible task-routing bottleneck +L12 35.1% Peak in middle layers +L15 22.1% +L18 19.1% +L20 20.3% +L23 52.9% Highest — output projection is very low-rank +``` + +Three regimes emerge: +1. **L0-L2**: Moderate PC1 (30-35%) — input encoding uses a few dominant directions +2. **L5-L20**: Lower PC1 (19-30%) — distributed computation uses more diverse directions, with a bump at L10-L12 +3. **L22-L23**: High PC1 (43-53%) — output formatting collapses to very few dimensions + +### Consecutive Layer Alignment + +``` +Pair Alignment +L0 -> L1 0.277 ######## +L1 -> L2 0.256 ####### +L4 -> L5 0.227 ###### +L8 -> L9 0.221 ###### +L10 -> L11 0.227 ###### +L11 -> L12 0.210 ###### +L12 -> L13 0.222 ###### +L13 -> L14 0.144 #### <-- MINIMUM +L14 -> L15 0.198 ##### +L17 -> L18 0.199 ##### +L20 -> L21 0.159 #### +L22 -> L23 0.131 ### <-- LOWEST +``` + +**Lowest alignment: L13->L14 (0.144) and L22->L23 (0.131).** + +These are the two points where the model's computation most sharply changes direction in residual stream space: + +- **L13->L14**: The boundary between "task classification" (L10-L13, high PC1) and "operand gathering / computation" (L14-L20). The subspace rotation here suggests a qualitative shift in what the layers are computing. +- **L22->L23**: The transition from computation to output projection. L23 writes in a nearly orthogonal direction to L22, consistent with the phase transition found in Analysis 2. + +No high-alignment non-consecutive layer pairs were detected, indicating each layer communicates primarily with its immediate neighbors rather than through long-range subspace channels. + +--- + +## Analysis 5: Bypass Validation (Causal) + +**Question:** If easy problems bypass middle/late computation, does skipping MoE at those layers hurt easy problems less than hard ones? + +**Method:** Monkey-patch the transformer blocks to skip the MoE sublayer (attention still runs normally) at specified layers. Compare first-token accuracy between baseline and skipped conditions for easy vs hard arithmetic. Uses few-shot prompting (`"Math: 2+2=4, 5*5=25, 10+10=20, "`) since GPT-OSS is a base model that requires context to produce arithmetic answers. + +### Baseline Accuracy + +| Category | Accuracy | Notes | +|----------|:--------:|-------| +| Easy arithmetic | **100%** (10/10) | All single/double-digit problems correct | +| Hard arithmetic | **30%** (3/10) | Only simpler hard problems: `234+567=801`, `512-178=334`, `1024-389=635` | + +Hard multiplication was never correct at baseline — the model gets the leading digits right (`47*47→220` for 2209, `89*73→649` for 6497) but truncates. Only 3-digit addition/subtraction problems were fully solved. + +### Skip Results + +| Condition | Layers Skipped | Easy Survival | Hard Survival | Gap | +|-----------|:---:|:---:|:---:|:---:| +| **skip_L0_L4** | 0-4 (input encoding) | **100%** (10/10) | **100%** (3/3) | **0%** | +| **skip_L10_L14** | 10-14 (task classification) | **100%** (10/10) | **100%** (3/3) | **0%** | +| **skip_L16_L20** | 16-20 (computation) | **50%** (5/10) | **0%** (0/3) | **+50%** | + +### The Critical Finding: L16-L20 + +Skipping MoE at L16-L20 produces a **+50% survival gap**, confirming the bypass hypothesis: + +**Easy problems that survived** (lookup pathway): +| Expression | Baseline | Skipped | Status | +|------------|:--------:|:-------:|:------:| +| `3*3` | 9 | 9 | Survived | +| `7*7` | 49 | 49 | Survived | +| `6+6` | 12 | 12 | Survived | +| `2*8` | 16 | 16 | Survived | +| `10*10`| 100 | 100 | Survived | + +These are **memorized facts** — perfect squares (`3*3`, `7*7`, `10*10`) and common products/sums. The answer is retrieved directly without needing MoE computation. + +**Easy problems that died** (still need computation): +| Expression | Baseline | Skipped | Failure Mode | +|------------|:--------:|:-------:|:-------------| +| `4+4` | 8 | **4** | Echoed first operand | +| `6*4` | 24 | **12** | Wrong answer | +| `8+7` | 15 | **8** | Echoed first operand | +| `9+1` | 10 | **9** | Echoed first operand | +| `5+3` | 8 | **3** | Echoed second operand | + +Without MoE computation layers, the model **defaults to echoing an input operand**. This is the degenerate behavior when the computation pathway is ablated but the lookup pathway has no stored answer. + +**Hard problems all died:** +| Expression | Baseline | Skipped | Failure Mode | +|------------|:--------:|:-------:|:-------------| +| `234+567` | 801 | **234** | Echoed first operand | +| `512-178` | 334 | (empty) | No output | +| `1024-389`| 635 | **102** | Echoed truncated first operand | + +Every hard problem that was correct at baseline became incorrect when L16-L20 MoE was skipped. + +### Control Conditions + +**skip_L0_L4** (input encoding): Zero impact. MoE at early layers is completely redundant for arithmetic. Attention alone at L0-L4 preserves the full computation chain. Minor digit-level perturbations are visible in hard problems (`89*73`: `649→651`, `156*23`: `358→359`) but these don't affect correctness. + +**skip_L10_L14** (task classification): Zero impact. The MoE contribution at these layers is informational rather than computational — the model classifies the task type here but doesn't perform arithmetic operations. Identical outputs to baseline for every single prompt. + +--- + +## Unified Mechanistic Picture + +Combining all five analyses produces a consistent functional map of the 24-layer architecture: + +``` + Layer Function Evidence + ----- -------------------- ---------------------------------------- + L0-L4 Input Encoding MoE redundant (Analysis 5: skip has no effect) + Moderate PC1 variance (Analysis 4: 30-35%) + Low residual change (Analysis 1) + + L5-L9 Task Routing Factual prompts diverge here (Analysis 1) + Operator tokens identified (Analysis 3: ~0.7 sim) + Decreasing subspace alignment (Analysis 4) + +L10-L13 Task Classification High PC1 (Analysis 4: 30-35%) — bottleneck + MoE redundant (Analysis 5: skip has no effect) + Factual prompts show elevated deltas (Analysis 1) + First operand dips to ~0.05 similarity (Analysis 3) + +L13-L14 BOUNDARY Lowest subspace alignment: 0.144 (Analysis 4) + Qualitative shift in computation direction + +L14-L17 Operand Gathering First operand re-gathered (Analysis 3: U-shape recovery) + Sharp jump in operand similarity at L17 (Analysis 3) + +L16-L20 Computation Hard > Easy residual deltas (Analysis 1) + MoE critical — skip destroys arithmetic (Analysis 5) + Lookup entries survive; computed answers die + Subtraction requires most computation (Analysis 1) + + L22 Phase Transition 3x jump in inter-layer delta (Analysis 2) + Second-lowest subspace alignment (Analysis 4) + Distance-to-final drops 0.43 → 0.22 in one step + +L22-L23 Output Formatting PC1 = 53% — very low-rank projection (Analysis 4) + Largest residual delta for all categories (Analysis 1) + All prompts converge here (Analysis 2) +``` + +### Key Findings + +1. **Dual-pathway arithmetic:** The model maintains two distinct mechanisms for arithmetic — a **lookup table** for memorized facts (survives MoE ablation) and a **computation pathway** through L16-L20 MoE experts (destroyed by ablation). The lookup pathway handles ~50% of "easy" single-digit arithmetic. + +2. **Operand echo as default:** When the computation pathway is ablated, the model falls back to echoing an input operand. This suggests the attention mechanism (which is preserved during MoE skip) copies operand tokens to the output position, but the MoE experts are needed to actually transform them into answers. + +3. **Asymmetric operand processing:** First operands undergo a U-shaped information flow trajectory (recognized → transformed → re-gathered), while second operands are gathered monotonically. This suggests sequential left-to-right processing of operands. + +4. **Two architectural boundaries:** L13→L14 and L22→L23 mark sharp subspace rotations, corresponding to the transitions from task-classification to computation, and from computation to output formatting. + +5. **MoE redundancy in early/middle layers:** L0-L14 MoE can be completely skipped with zero impact on arithmetic accuracy. The attention mechanism alone is sufficient for input encoding, task routing, and task classification. MoE becomes essential only at L16+. + +6. **Late convergence:** The residual stream does not stabilize until the final layers (L22-L23), with a single phase transition at L22. Computation is distributed across all layers but output formatting is concentrated. + +--- + +## Methodology Notes + +- **Model architecture:** GptOssBlock = Pre-norm → Attention → Residual Add → Pre-norm → MoE → Residual Add +- **Layer skipping implementation:** Monkey-patches `block.__call__` to skip the MoE sublayer while preserving attention. At skipped layers: `x = x + attention(layernorm(x))` (MoE term omitted). +- **Few-shot prompting:** GPT-OSS is a base model (not instruction-tuned) and produces degenerate output for bare arithmetic prompts. All bypass validation uses the prefix `"Math: 2+2=4, 5*5=25, 10+10=20, "` to elicit arithmetic answers. +- **Correctness checking:** First-token greedy decoding. Easy problems checked for exact match; hard problems checked for leading-digit match (the model often gets the first 2-3 digits right but truncates). +- **bfloat16 handling:** MLX uses bfloat16 internally. All numpy operations require `.astype(mx.float32)` conversion first. +- **Information flow proxy:** Uses cosine similarity between position representations rather than attention weights. This captures representational convergence regardless of mechanism (attention, MoE routing, or residual bypass). + +## Data Files + +- `results/results_20260201_235243.json` — Analyses 1-4 (bypass detection, residual saturation, information flow, layer subspaces) +- `results/results_20260202_000906.json` — Analysis 5 (bypass validation / causal layer skipping) From 27ad9d3be2b44f7674e11a1e3b5bf9aa5149261b Mon Sep 17 00:00:00 2001 From: chrishayuk Date: Mon, 2 Feb 2026 00:34:34 +0000 Subject: [PATCH 3/5] updated resultls --- .../mb_attention_at_l20.py | 767 ++++++++++++++++++ .../expert_function_classification/results.md | 92 ++- 2 files changed, 855 insertions(+), 4 deletions(-) create mode 100644 experiments/expert_function_classification/mb_attention_at_l20.py diff --git a/experiments/expert_function_classification/mb_attention_at_l20.py b/experiments/expert_function_classification/mb_attention_at_l20.py new file mode 100644 index 00000000..08522b64 --- /dev/null +++ b/experiments/expert_function_classification/mb_attention_at_l20.py @@ -0,0 +1,767 @@ +#!/usr/bin/env python3 +"""Memory Bank Attention at L20 Experiment. + +The memory bank injection point experiment revealed a striking puzzle: +at L20, bare prompts show 14.8% fact probability while MB prompts show +only 0.05% — despite the answer being explicitly in context. + +This experiment captures attention weights at L19-L22 for both bare and +MB conditions, and asks: where is the final token attending at L20 in +the MB condition? Three hypotheses: + + 1. Distraction: final token attends to instruction tokens ([Memory Bank], + [End], "Using...answer:") rather than the answer "Paris" + 2. Interference: final token attends to "Paris" in MB but the + representation is in the wrong subspace for retrieval + 3. Delayed integration: model ignores in-context "Paris" at L20 + entirely, only integrating at L21 + +Method: + For each fact, run bare and MB prompts through the attention capture + monkey-patch. Classify each token position into semantic regions: + - MB_ANSWER: the answer token in the memory bank (e.g., "Paris") + - MB_ENTITY: the entity token in the memory bank (e.g., "France") + - MB_OTHER: other memory bank entry tokens + - MB_DELIMITERS: [Memory Bank], [End Memory Bank] + - INSTRUCTION: "Using the memory bank above, answer:" + - QUERY: the query text (e.g., "The capital of France is") + - QUERY_ENTITY: entity within the query + - QUERY_COPULA: " is" at end of query + - ANSWER_PREFIX: "Answer:" suffix + + Report attention to each region at L19, L20, L21, L22. + +Run: python experiments/expert_function_classification/mb_attention_at_l20.py +""" + +from __future__ import annotations + +import asyncio +import json +import logging +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any + +import mlx.core as mx + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) +logger = logging.getLogger(__name__) + + +FACTS = [ + { + "prompt": "The capital of France is", + "expected_keyword": "Paris", + "entity": "France", + "mb_entry": "France | capital | Paris", + "mb_answer": "Paris", + }, + { + "prompt": "The chemical symbol for gold is", + "expected_keyword": "Au", + "entity": "gold", + "mb_entry": "Gold | chemical symbol | Au", + "mb_answer": "Au", + }, + { + "prompt": "The author of Romeo and Juliet is", + "expected_keyword": "Shakespeare", + "entity": "Romeo", + "mb_entry": "Romeo and Juliet | author | William Shakespeare", + "mb_answer": "William", + }, + { + "prompt": "The CEO of Microsoft is", + "expected_keyword": "Nadella", + "entity": "Microsoft", + "mb_entry": "Microsoft | CEO | Satya Nadella", + "mb_answer": "Sat", + }, + { + "prompt": "The capital of Japan is", + "expected_keyword": "Tokyo", + "entity": "Japan", + "mb_entry": "Japan | capital | Tokyo", + "mb_answer": "Tokyo", + }, + { + "prompt": "The chemical symbol for silver is", + "expected_keyword": "Ag", + "entity": "silver", + "mb_entry": "Silver | chemical symbol | Ag", + "mb_answer": "Ag", + }, + { + "prompt": "The capital of Australia is", + "expected_keyword": "Canberra", + "entity": "Australia", + "mb_entry": "Australia | capital | Canberra", + "mb_answer": "Canberra", + }, +] + +# Focus on these layers (emergence zone) +FOCUS_LAYERS = [18, 19, 20, 21, 22, 23] + + +def build_memory_bank_prompt(question: str, mb_entries: list[str]) -> str: + """Build a prompt with memory bank injection.""" + mb_block = "\n".join(f"- {entry}" for entry in mb_entries) + return ( + f"[Memory Bank]\n" + f"{mb_block}\n" + f"[End Memory Bank]\n\n" + f"Using the memory bank above, answer: {question}\n" + f"Answer:" + ) + + +class MBAttentionAtL20: + """Compare attention patterns between bare and MB conditions at L20.""" + + def __init__(self): + self.model = None + self.tokenizer = None + self._attn_class = None + self._original_attn_call = None + + async def setup(self): + from chuk_lazarus.introspection.moe.expert_router import ExpertRouter + + logger.info("Loading model: openai/gpt-oss-20b") + router = await ExpertRouter.from_pretrained("openai/gpt-oss-20b") + self.model = router._model + self.tokenizer = router._tokenizer + + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + + mx.eval(self.model.parameters()) + + sample_layer = self.model.model.layers[0] + self._attn_class = type(sample_layer.self_attn) + self._original_attn_call = self._attn_class.__call__ + + self.num_layers = len(self.model.model.layers) + logger.info(f"Model loaded: {self.num_layers} layers. Ready.") + + def _capture_attention_forward(self, prompt: str) -> dict[int, mx.array]: + """Run forward pass capturing attention weights at focus layers. + + Returns {layer_idx: weights} where weights shape is + [num_kv_groups, q_len, kv_len], averaged across heads in each group. + """ + captured: dict[int, mx.array] = {} + original_call = self._original_attn_call + focus = set(FOCUS_LAYERS) + + def patched_attn( + attn_self: Any, + x: mx.array, + mask: mx.array | str | None = None, + cache: tuple[mx.array, mx.array] | None = None, + ) -> tuple[mx.array, tuple[mx.array, mx.array] | None]: + batch, seq_len, _ = x.shape + + q = attn_self.q_proj(x) + k = attn_self.k_proj(x) + v = attn_self.v_proj(x) + + q = q.reshape(batch, seq_len, attn_self.num_heads, attn_self.head_dim) + k = k.reshape(batch, seq_len, attn_self.num_kv_heads, attn_self.head_dim) + v = v.reshape(batch, seq_len, attn_self.num_kv_heads, attn_self.head_dim) + + q = q.transpose(0, 2, 1, 3) + k = k.transpose(0, 2, 1, 3) + v = v.transpose(0, 2, 1, 3) + + if cache is not None: + q = attn_self.rope(q, offset=cache[0].shape[2]) + k = attn_self.rope(k, offset=cache[0].shape[2]) + else: + q = attn_self.rope(q) + k = attn_self.rope(k) + + if cache is not None: + k = mx.concatenate([cache[0], k], axis=2) + v = mx.concatenate([cache[1], v], axis=2) + new_cache = (k, v) + + layer_idx = attn_self.layer_idx + + # Only compute attention weights for focus layers + if layer_idx in focus: + num_groups = attn_self.num_heads // attn_self.num_kv_heads + k_expanded = mx.repeat(k, num_groups, axis=1) + scores = (q @ k_expanded.transpose(0, 1, 3, 2)) * attn_self.scale + if mask is not None and not isinstance(mask, str): + scores = scores + mask + weights = mx.softmax(scores, axis=-1) + weights_grouped = weights.reshape( + batch, attn_self.num_kv_heads, num_groups, seq_len, -1 + ) + weights_avg = mx.mean(weights_grouped, axis=2) + captured[layer_idx] = mx.stop_gradient(weights_avg[0]) + + output = mx.fast.scaled_dot_product_attention( + q, new_cache[0], new_cache[1], + scale=attn_self.scale, + mask=mask, + sinks=attn_self.sinks, + ) + output = output.transpose(0, 2, 1, 3) + output = output.reshape(batch, seq_len, -1) + output = attn_self.o_proj(output) + + return output, new_cache + + input_ids = mx.array(self.tokenizer.encode(prompt))[None, :] + + try: + self._attn_class.__call__ = patched_attn + self.model(input_ids) + mx.eval(list(captured.values())) + finally: + self._attn_class.__call__ = self._original_attn_call + + return captured + + def _find_token_positions(self, token_ids: list[int], search_str: str) -> list[int]: + """Find positions of tokens that decode to contain search_str.""" + positions = [] + for i, tid in enumerate(token_ids): + decoded = self.tokenizer.decode([tid]) + if search_str.lower() in decoded.lower(): + positions.append(i) + return positions + + def _classify_mb_regions( + self, mb_prompt: str, fact: dict + ) -> dict[str, list[int]]: + """Classify each token position in the MB prompt into semantic regions. + + Returns dict of region_name -> list of token positions. + """ + token_ids = self.tokenizer.encode(mb_prompt) + token_strs = [self.tokenizer.decode([tid]) for tid in token_ids] + n = len(token_ids) + + # Build the full text position-by-position to find region boundaries + # Strategy: tokenize sub-parts to find boundaries + all_mb_entries = [f["mb_entry"] for f in FACTS] + mb_block = "\n".join(f"- {entry}" for entry in all_mb_entries) + mb_header = "[Memory Bank]\n" + mb_footer = "\n[End Memory Bank]\n\n" + instruction = f"Using the memory bank above, answer: " + query = fact["prompt"] + answer_suffix = "\nAnswer:" + + # Tokenize each section to find approximate boundaries + header_ids = self.tokenizer.encode(mb_header, add_special_tokens=False) + block_ids = self.tokenizer.encode(mb_block, add_special_tokens=False) + footer_ids = self.tokenizer.encode(mb_footer, add_special_tokens=False) + instruction_ids = self.tokenizer.encode(instruction, add_special_tokens=False) + query_ids = self.tokenizer.encode(query, add_special_tokens=False) + answer_ids = self.tokenizer.encode(answer_suffix, add_special_tokens=False) + + # Approximate positions (tokenization of parts may differ from whole) + # Use cumulative lengths + header_end = len(header_ids) + block_end = header_end + len(block_ids) + footer_end = block_end + len(footer_ids) + instruction_end = footer_end + len(instruction_ids) + query_end = instruction_end + len(query_ids) + + regions: dict[str, list[int]] = { + "mb_delimiters": [], + "mb_answer": [], + "mb_entity": [], + "mb_other": [], + "instruction": [], + "query_entity": [], + "query_copula": [], + "query_other": [], + "answer_prefix": [], + } + + # Find answer and entity positions within MB block + mb_answer_str = fact["mb_answer"] + mb_entity_str = fact["entity"] + + # First pass: assign regions by position ranges + for i in range(n): + if i < header_end: + regions["mb_delimiters"].append(i) + elif i < block_end: + regions["mb_other"].append(i) # default; override below + elif i < footer_end: + regions["mb_delimiters"].append(i) + elif i < instruction_end: + regions["instruction"].append(i) + elif i < query_end: + regions["query_other"].append(i) # default; override below + else: + regions["answer_prefix"].append(i) + + # Second pass: find specific tokens within MB block + # Search for answer token in MB entries region + for i in regions["mb_other"][:]: + decoded = token_strs[i].strip() + if decoded and mb_answer_str.lower().startswith(decoded.lower()): + regions["mb_other"].remove(i) + regions["mb_answer"].append(i) + elif decoded and mb_entity_str.lower() in decoded.lower(): + regions["mb_other"].remove(i) + regions["mb_entity"].append(i) + + # Third pass: find entity and copula in query + for i in regions["query_other"][:]: + decoded = token_strs[i] + if fact["entity"].lower() in decoded.lower(): + regions["query_other"].remove(i) + regions["query_entity"].append(i) + elif decoded.strip() == "is": + regions["query_other"].remove(i) + regions["query_copula"].append(i) + + return regions + + def _classify_bare_regions( + self, bare_prompt: str, fact: dict + ) -> dict[str, list[int]]: + """Classify token positions in bare prompt.""" + token_ids = self.tokenizer.encode(bare_prompt) + token_strs = [self.tokenizer.decode([tid]) for tid in token_ids] + n = len(token_ids) + + regions: dict[str, list[int]] = { + "query_entity": [], + "query_copula": [], + "query_other": [], + } + + for i in range(n): + decoded = token_strs[i] + if fact["entity"].lower() in decoded.lower(): + regions["query_entity"].append(i) + elif decoded.strip() == "is": + regions["query_copula"].append(i) + else: + regions["query_other"].append(i) + + return regions + + def _region_attention( + self, + weights: mx.array, + regions: dict[str, list[int]], + last_pos: int, + ) -> dict[str, float]: + """Compute attention from last position to each region. + + weights shape: [kv_groups, seq_len, seq_len] + Returns {region_name: total_attention_to_region}. + """ + # Average across KV groups for final-token attention + final_attn = mx.mean(weights[:, last_pos, :], axis=0) # [seq_len] + final_attn_list = final_attn.tolist() + + result = {} + for region_name, positions in regions.items(): + if positions: + total = sum(final_attn_list[p] for p in positions if p < len(final_attn_list)) + result[region_name] = round(total, 6) + else: + result[region_name] = 0.0 + + return result + + async def analyze_fact(self, fact: dict) -> dict[str, Any]: + """Compare attention in bare vs MB conditions for one fact.""" + prompt = fact["prompt"] + entity = fact["entity"] + mb_answer_str = fact["mb_answer"] + + logger.info(f"\n Fact: {prompt} (entity='{entity}', answer='{mb_answer_str}')") + + # Build prompts + bare_prompt = prompt + all_mb_entries = [f["mb_entry"] for f in FACTS] + mb_prompt = build_memory_bank_prompt(prompt, all_mb_entries) + + bare_tokens = self.tokenizer.encode(bare_prompt) + mb_tokens = self.tokenizer.encode(mb_prompt) + bare_strs = [self.tokenizer.decode([t]) for t in bare_tokens] + mb_strs = [self.tokenizer.decode([t]) for t in mb_tokens] + + logger.info(f" Bare: {len(bare_tokens)} tokens") + logger.info(f" MB: {len(mb_tokens)} tokens") + + # Classify regions + bare_regions = self._classify_bare_regions(bare_prompt, fact) + mb_regions = self._classify_mb_regions(mb_prompt, fact) + + # Log region mapping + for rname, positions in mb_regions.items(): + if positions: + sample = [mb_strs[p] for p in positions[:3]] + logger.info(f" MB region '{rname}': {len(positions)} tokens, e.g. {sample}") + + # Capture attention for both conditions + loop = asyncio.get_event_loop() + bare_attn = await loop.run_in_executor( + None, self._capture_attention_forward, bare_prompt + ) + mb_attn = await loop.run_in_executor( + None, self._capture_attention_forward, mb_prompt + ) + + bare_last = len(bare_tokens) - 1 + mb_last = len(mb_tokens) - 1 + + # Compute region attention at each focus layer + layer_results = {} + for layer_idx in FOCUS_LAYERS: + bare_layer = {} + mb_layer = {} + + if layer_idx in bare_attn: + bare_layer = self._region_attention( + bare_attn[layer_idx], bare_regions, bare_last + ) + if layer_idx in mb_attn: + mb_layer = self._region_attention( + mb_attn[layer_idx], mb_regions, mb_last + ) + + layer_results[layer_idx] = { + "bare": bare_layer, + "mb": mb_layer, + } + + # Log comparison + logger.info(f" L{layer_idx}:") + if bare_layer: + qe = bare_layer.get("query_entity", 0) + qc = bare_layer.get("query_copula", 0) + qo = bare_layer.get("query_other", 0) + logger.info(f" Bare: entity={qe:.4f} copula={qc:.4f} other={qo:.4f}") + if mb_layer: + mba = mb_layer.get("mb_answer", 0) + mbe = mb_layer.get("mb_entity", 0) + mbo = mb_layer.get("mb_other", 0) + mbd = mb_layer.get("mb_delimiters", 0) + ins = mb_layer.get("instruction", 0) + qe = mb_layer.get("query_entity", 0) + qc = mb_layer.get("query_copula", 0) + qo = mb_layer.get("query_other", 0) + ap = mb_layer.get("answer_prefix", 0) + logger.info( + f" MB: mb_answer={mba:.4f} mb_entity={mbe:.4f} " + f"mb_other={mbo:.4f} delims={mbd:.4f}" + ) + logger.info( + f" instruction={ins:.4f} q_entity={qe:.4f} " + f"q_copula={qc:.4f} q_other={qo:.4f} answer_pfx={ap:.4f}" + ) + + # Also get per-position attention at L20 for detailed analysis + l20_per_position = {} + if 20 in mb_attn: + final_attn_l20 = mx.mean(mb_attn[20][:, mb_last, :], axis=0).tolist() + # Top-5 positions by attention at L20 + indexed = [(i, v) for i, v in enumerate(final_attn_l20)] + indexed.sort(key=lambda x: x[1], reverse=True) + l20_top5 = [] + for pos, val in indexed[:10]: + tok = mb_strs[pos] if pos < len(mb_strs) else "?" + l20_top5.append({"position": pos, "token": tok, "attention": round(val, 6)}) + logger.info(f" L20 top attn: pos={pos} token='{tok}' attn={val:.4f}") + l20_per_position = { + "top_10": l20_top5, + "full_distribution": { + str(i): round(v, 6) for i, v in enumerate(final_attn_l20) + }, + } + + return { + "prompt": prompt, + "entity": entity, + "mb_answer": mb_answer_str, + "bare_tokens": bare_strs, + "mb_tokens": mb_strs, + "bare_regions": {k: v for k, v in bare_regions.items()}, + "mb_regions": {k: v for k, v in mb_regions.items()}, + "layer_results": { + str(k): v for k, v in layer_results.items() + }, + "l20_mb_detail": l20_per_position, + } + + def _compute_summary(self, fact_results: list[dict]) -> dict[str, Any]: + """Compute aggregate attention statistics.""" + valid = [r for r in fact_results if "error" not in r] + + # Average region attention by layer for MB condition + avg_mb_regions: dict[int, dict[str, list[float]]] = defaultdict(lambda: defaultdict(list)) + avg_bare_regions: dict[int, dict[str, list[float]]] = defaultdict(lambda: defaultdict(list)) + + for r in valid: + for layer_str, layer_data in r["layer_results"].items(): + layer_idx = int(layer_str) + for region, attn in layer_data.get("mb", {}).items(): + avg_mb_regions[layer_idx][region].append(attn) + for region, attn in layer_data.get("bare", {}).items(): + avg_bare_regions[layer_idx][region].append(attn) + + # Compute averages + avg_mb = {} + for layer_idx in sorted(avg_mb_regions.keys()): + avg_mb[layer_idx] = { + region: round(sum(vals) / len(vals), 6) + for region, vals in avg_mb_regions[layer_idx].items() + } + + avg_bare = {} + for layer_idx in sorted(avg_bare_regions.keys()): + avg_bare[layer_idx] = { + region: round(sum(vals) / len(vals), 6) + for region, vals in avg_bare_regions[layer_idx].items() + } + + # Key comparison: MB attention to answer token at L20 vs L21 + mb_answer_l20 = avg_mb.get(20, {}).get("mb_answer", 0) + mb_answer_l21 = avg_mb.get(21, {}).get("mb_answer", 0) + mb_instruction_l20 = avg_mb.get(20, {}).get("instruction", 0) + mb_delimiters_l20 = avg_mb.get(20, {}).get("mb_delimiters", 0) + + return { + "num_facts": len(valid), + "avg_mb_attention_by_layer": avg_mb, + "avg_bare_attention_by_layer": avg_bare, + "l20_diagnosis": { + "mb_answer_attention_l20": mb_answer_l20, + "mb_answer_attention_l21": mb_answer_l21, + "mb_instruction_attention_l20": mb_instruction_l20, + "mb_delimiters_attention_l20": mb_delimiters_l20, + "interpretation": ( + "Distraction" if mb_instruction_l20 + mb_delimiters_l20 > 0.3 + else "Delayed integration" if mb_answer_l20 < 0.02 + else "Interference" if mb_answer_l20 > 0.05 + else "Unknown" + ), + }, + } + + def _print_summary(self, summary: dict, fact_results: list[dict]): + valid = [r for r in fact_results if "error" not in r] + + print("\n" + "=" * 110) + print("MEMORY BANK ATTENTION AT L20 - RESULTS") + print("=" * 110) + + # Average MB attention by region and layer + print("\n" + "-" * 110) + print("AVERAGE MB CONDITION: ATTENTION BY REGION AND LAYER") + print("-" * 110) + + regions = [ + "mb_answer", "mb_entity", "mb_other", "mb_delimiters", + "instruction", "query_entity", "query_copula", "query_other", "answer_prefix", + ] + header = f"{'Layer':>5}" + for r in regions: + short = r.replace("mb_", "mb:").replace("query_", "q:") + header += f" | {short:>12}" + print(header) + print("-" * 110) + + avg_mb = summary["avg_mb_attention_by_layer"] + for layer_idx in sorted(avg_mb.keys()): + row = f"L{layer_idx:>3}:" + for r in regions: + val = avg_mb[layer_idx].get(r, 0) + row += f" | {val:>12.4f}" + marker = "" + if layer_idx == 20: + marker = " <- L20 DIP" + print(f"{row}{marker}") + + # Average bare attention for comparison + print("\n" + "-" * 110) + print("AVERAGE BARE CONDITION: ATTENTION BY REGION AND LAYER") + print("-" * 110) + + bare_regions = ["query_entity", "query_copula", "query_other"] + header = f"{'Layer':>5}" + for r in bare_regions: + short = r.replace("query_", "q:") + header += f" | {short:>12}" + print(header) + print("-" * 60) + + avg_bare = summary["avg_bare_attention_by_layer"] + for layer_idx in sorted(avg_bare.keys()): + row = f"L{layer_idx:>3}:" + for r in bare_regions: + val = avg_bare[layer_idx].get(r, 0) + row += f" | {val:>12.4f}" + print(row) + + # L20 diagnosis + print("\n" + "-" * 110) + print("L20 DIAGNOSIS") + print("-" * 110) + + diag = summary["l20_diagnosis"] + print(f"\n At L20, the final token in MB condition attends to:") + print(f" Answer token in MB ('Paris' etc): {diag['mb_answer_attention_l20']:.4f}") + print(f" Instruction text: {diag['mb_instruction_attention_l20']:.4f}") + print(f" MB delimiters: {diag['mb_delimiters_attention_l20']:.4f}") + print(f"\n At L21 (where MB catches up):") + print(f" Answer token in MB: {diag['mb_answer_attention_l21']:.4f}") + + print(f"\n Diagnosis: {diag['interpretation']}") + + # Per-fact L20 top attention targets + print("\n" + "-" * 110) + print("PER-FACT: TOP ATTENTION TARGETS AT L20 (MB CONDITION)") + print("-" * 110) + + for r in valid: + prompt_short = r["prompt"][:35] + print(f"\n {prompt_short} (answer='{r['mb_answer']}'):") + if "l20_mb_detail" in r and r["l20_mb_detail"]: + for entry in r["l20_mb_detail"]["top_10"][:5]: + tok = entry["token"].replace("\n", "\\n") + print(f" pos {entry['position']:>3}: '{tok:<15}' attn={entry['attention']:.4f}") + + # Entity attention comparison: bare vs MB + print("\n" + "-" * 110) + print("ENTITY ATTENTION COMPARISON (final token -> entity in query)") + print("-" * 110) + print(f"\n {'Layer':>5} | {'Bare q_entity':>14} | {'MB q_entity':>14} | {'MB mb_answer':>14}") + print(" " + "-" * 60) + + for layer_idx in FOCUS_LAYERS: + bare_qe = avg_bare.get(layer_idx, {}).get("query_entity", 0) + mb_qe = avg_mb.get(layer_idx, {}).get("query_entity", 0) + mb_ans = avg_mb.get(layer_idx, {}).get("mb_answer", 0) + print(f" L{layer_idx:>3}: {bare_qe:>14.4f} | {mb_qe:>14.4f} | {mb_ans:>14.4f}") + + # Key findings + print("\n" + "=" * 110) + print("KEY FINDINGS") + print("=" * 110) + + interpretation = diag["interpretation"] + if interpretation == "Distraction": + print( + "\n The L20 dip is caused by DISTRACTION." + ) + print( + " At L20, the MB condition attends heavily to instruction tokens and" + ) + print( + " delimiters rather than the answer or entity. The explicit context" + ) + print( + " draws attention away from the retrieval pathway." + ) + elif interpretation == "Delayed integration": + print( + "\n The L20 dip is caused by DELAYED INTEGRATION." + ) + print( + " At L20, the model doesn't attend to the MB answer token." + ) + print( + " The in-context answer is effectively ignored until L21." + ) + print( + " The model only integrates the explicit context after" + ) + print( + " completing its own retrieval computation." + ) + elif interpretation == "Interference": + print( + "\n The L20 dip is caused by INTERFERENCE." + ) + print( + " At L20, the model attends to the MB answer but the" + ) + print( + " representation interferes with the retrieval pathway." + ) + + print("=" * 110) + + def _save_results(self, results: dict) -> Path: + results_dir = Path(__file__).parent / "results" + results_dir.mkdir(exist_ok=True) + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = results_dir / f"mb_attention_at_l20_{timestamp}.json" + + with open(output_path, "w") as f: + json.dump(results, f, indent=2, default=str) + logger.info(f"\nResults saved to {output_path}") + return output_path + + async def run(self): + await self.setup() + + logger.info("=" * 70) + logger.info("MEMORY BANK ATTENTION AT L20") + logger.info(f" Facts: {len(FACTS)}") + logger.info(f" Focus layers: {FOCUS_LAYERS}") + logger.info(f" Comparing: bare vs memory bank attention patterns") + logger.info("=" * 70) + + fact_results = [] + for fact in FACTS: + result = await self.analyze_fact(fact) + fact_results.append(result) + + summary = self._compute_summary(fact_results) + + output = { + "metadata": { + "experiment": "mb_attention_at_l20", + "model": "openai/gpt-oss-20b", + "timestamp": datetime.now().isoformat(), + "num_facts": len(FACTS), + "focus_layers": FOCUS_LAYERS, + "description": ( + "Compares attention patterns between bare and MB prompts " + "at L19-L22. Diagnoses why MB probability dips to 0.05% " + "at L20 while bare shows 14.8%. Tests distraction vs " + "interference vs delayed integration hypotheses." + ), + "prior_results": { + "mb_injection_point": "MB does NOT shift emergence; L20 probability dip", + "attention_at_emergence": "Entity attention peaks L19/L21; 1.3x increase", + "bare_l20_probability": "0.1479", + "mb_l20_probability": "0.0005", + }, + }, + "summary": summary, + "fact_results": fact_results, + } + + self._save_results(output) + self._print_summary(summary, fact_results) + + +async def main(): + experiment = MBAttentionAtL20() + await experiment.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/experiments/expert_function_classification/results.md b/experiments/expert_function_classification/results.md index bff85d44..01bc2f71 100644 --- a/experiments/expert_function_classification/results.md +++ b/experiments/expert_function_classification/results.md @@ -2,7 +2,7 @@ **Model**: GPT-OSS 20B (24 MoE layers, 32 experts/layer, top-4 routing, ~21B params) **Date**: 29 Jan - 2 Feb 2026 -**Experiments run**: 19 of 20 designed (15 classification + 4 residual stream mechanistic) +**Experiments run**: 20 of 21 designed (15 classification + 5 residual stream mechanistic) --- @@ -487,9 +487,88 @@ The L22 dip for MB (37.1% vs 70.0% bare) may reflect the model processing the in --- +## Part 5d: Memory Bank Attention at L20 (2 Feb) + +### Setup + +The MB injection point experiment (Part 5c) showed a striking L20 probability dip: bare prompts have 14.8% fact probability at L20, while MB prompts have 0.05%. This experiment captures attention weights at L18-L23 for both conditions and classifies every MB token into semantic regions to diagnose why. + +Regions: mb_answer (e.g., "Paris"), mb_entity (e.g., "France"), mb_other (other MB entries), mb_delimiters ([Memory Bank], [End Memory Bank]), instruction ("Using the memory bank above, answer:"), query_entity, query_copula (" is"), query_other, answer_prefix ("Answer:"). + +### The Alternating Pattern + +The data reveals the same odd/even alternation found in Part 5b (attention_at_emergence), but in an MB-amplified form: + +| Layer | MB Answer | MB Other | Answer Prefix | Instruction | Q Entity | Q Copula | +|-------|-----------|----------|---------------|-------------|----------|----------| +| L18 (even) | 0.014 | 0.077 | **0.370** | 0.103 | 0.085 | 0.104 | +| L19 (odd) | **0.098** | **0.346** | 0.128 | 0.091 | 0.043 | 0.051 | +| L20 (even) | 0.016 | 0.082 | **0.413** | 0.110 | 0.045 | 0.089 | +| L21 (odd) | **0.134** | **0.318** | 0.113 | 0.108 | 0.036 | 0.042 | +| L22 (even) | 0.012 | 0.091 | **0.495** | 0.120 | 0.022 | 0.033 | +| L23 (odd) | **0.098** | **0.310** | 0.178 | 0.104 | 0.025 | 0.052 | + +**Even layers (L18, L20, L22):** "Answer:" prefix absorbs 37-49% of attention. MB answer token gets <2%. +**Odd layers (L19, L21, L23):** MB content dominates. mb_answer gets 10-13%, mb_other gets 31-35%. Answer prefix drops to 11-18%. + +### L20 Top Attention Targets (MB Condition) + +Across all 7 facts, the top-5 most-attended positions at L20 are identical in structure: + +| Rank | Token | Avg Attention | Role | +|------|-------|---------------|------| +| 1 | `:` | 0.280 | Answer prefix | +| 2 | `Answer` | 0.132 | Answer prefix | +| 3 | `\n` | 0.120 | Answer prefix | +| 4 | ` is` | 0.089 | Query copula | +| 5 | entity | 0.052 | Query entity | + +**Total attention to "Answer:" at L20: 41.3%.** The model is processing "what kind of answer do I need?" not "what is the answer?" + +The MB answer token ("Paris", "Au", etc.) receives only **1.5% attention at L20** — effectively ignored. + +### The L21 Switch + +At L21, the pattern reverses: +- MB answer attention: 1.5% → **13.4%** (9x increase) +- MB other attention: 8.2% → **31.8%** (4x increase) +- Answer prefix attention: 41.3% → **11.3%** (73% drop) + +The model switches from task framing to content lookup in a single layer transition. + +### Connection to Part 5b (Bare Condition) + +The bare condition shows the same alternation: +- L20 bare: copula (" is") gets 40.2%, entity gets 25.6% +- L21 bare: entity gets 30.6%, copula gets 26.8% + +In bare prompts, the copula " is" plays the same role as "Answer:" in MB prompts — both are task-framing tokens. But in bare prompts, even at L20 the entity gets 25.6% because there are only 5 tokens competing for attention. In MB prompts, attention is diluted across 82 tokens, and "Answer:" — which is the strongest task-framing signal — absorbs 41.3%. + +### Diagnosis + +The L20 probability dip is **not distraction, not interference, but a structural property of the alternating attention architecture.** Even layers are task-framing layers. The MB prompt amplifies this effect because: + +1. "Answer:" is a stronger task-framing signal than " is" (it's an explicit instruction) +2. The MB content (82 tokens) dilutes entity attention from 25.6% (bare) to 4.5% (MB) +3. At L20, the model hasn't yet read the MB content — that happens at L19/L21 + +The model deliberately delays MB content integration to odd layers. This is not a failure; it's the same two-phase cycle (frame → lookup → frame → lookup) running on a longer context. + +### Conclusions + +1. **The L20 dip is explained by the odd/even attention architecture.** Even layers frame the task; odd layers read content. "Answer:" acts as a super-copula at even layers. + +2. **MB answer token attention oscillates: 1.5% (L20) → 13.4% (L21) → 1.2% (L22) → 9.8% (L23).** The model reads the MB answer at odd layers only. + +3. **This explains why MB catches up at L21.** At L21, the model finally reads the MB content (13.4% to answer, 31.8% to other entries), processes it through the MoE FFN, and writes the fact to the residual stream. This is exactly one layer behind bare (which crystallizes at L20), explaining the slight probability lag. + +4. **"Answer:" serves a critical role in MB integration.** It provides the task-framing signal that tells the model "produce the answer now." Without it, the model might not know when to switch from MB reading to answer generation. This explains why the MB format includes explicit instruction text. + +--- + ## Part 6: Synthesis -### The Five-Part Story +### The Six-Part Story These experiments reveal a consistent architecture: @@ -513,7 +592,11 @@ At L19 and L21, the prediction token focuses on the entity token (France, gold, MB does NOT shift fact emergence earlier. Despite providing the answer explicitly in context, facts still crystallize at L21-23. The bare and MB residual streams start completely different (cosine distance 0.57 at L0) but converge monotonically to near-identical representations by L23 (distance 0.11). MB is actually *slower* at L20 (0.05% vs 14.8% bare), catching up at L21. MB provides a redundant computation pathway, not a shortcut. -**6. Routing can be frozen at non-critical layers because facts don't depend on individual expert selection.** +**6. The MB probability dip at L20 is explained by the odd/even attention architecture.** + +At even layers (L20, L22), attention focuses on "Answer:" (41-49%), the task-framing signal. At odd layers (L19, L21, L23), attention switches to MB content — the answer token jumps from 1.5% to 13.4% between L20 and L21 (9x increase). This is the same frame→lookup cycle seen in bare prompts (copula focus at L20, entity focus at L21), amplified by the explicit instruction text. The model reads MB content only at odd layers, explaining the one-layer delay. + +**7. Routing can be frozen at non-critical layers because facts don't depend on individual expert selection.** The minimum viable routing experiments show that 6-7 learned layers + memory bank injection = 100% fact preservation. The critical layers align with the crystallization zone: configs that include learned routing at L19+ succeed; configs that skip this zone fail. @@ -609,4 +692,5 @@ Expert parameters account for ~85% of the 21B parameter model (~17.8B). With rou | 17 | layer_skip_emergence | 2 Feb | L20+L21 skip: 5/7 facts survive; robustness correlates with competitive margin | | 18 | attention_at_emergence | 2 Feb | Entity attention peaks at L19/L21 (1.3x); alternates with " is" focus at L20/L22 | | 19 | memory_bank_injection_point | 2 Feb | MB does NOT shift emergence; representations converge L16-L23; MB slower at L20 | -| 20 | attention_head_ablation | -- | Designed, not run | +| 20 | mb_attention_at_l20 | 2 Feb | L20 dip = odd/even alternation; "Answer:" absorbs 41% at L20; mb_answer 9x jump at L21 | +| 21 | attention_head_ablation | -- | Designed, not run | From 26ba883833c7b0a34dd659b733fcff0ee9cbfdd8 Mon Sep 17 00:00:00 2001 From: chrishayuk Date: Wed, 4 Mar 2026 22:41:16 +0000 Subject: [PATCH 4/5] updated interventions --- .../introspection/interventions.py | 29 ++++++++++++++----- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/src/chuk_lazarus/introspection/interventions.py b/src/chuk_lazarus/introspection/interventions.py index 03b40ff8..152eb327 100644 --- a/src/chuk_lazarus/introspection/interventions.py +++ b/src/chuk_lazarus/introspection/interventions.py @@ -293,12 +293,19 @@ def __init__( self._original_layers: dict[int, Any] = {} def _detect_structure(self) -> None: - """Detect model structure.""" + """Detect model structure. + + IMPORTANT: ``self._layers`` must be a *reference* to the actual + model layer list, **not** a copy. ``intervened_forward`` swaps + entries in this list with ``InterventionWrapper`` objects; if + it were a copy the model's forward pass would never see the + wrappers and all interventions would silently be no-ops. + """ if hasattr(self.model, "model") and hasattr(self.model.model, "layers"): - self._layers = list(self.model.model.layers) + self._layers = self.model.model.layers # reference, NOT list() self._backbone = self.model.model elif hasattr(self.model, "layers"): - self._layers = list(self.model.layers) + self._layers = self.model.layers # reference, NOT list() self._backbone = self.model else: raise ValueError("Cannot detect model layer structure") @@ -534,6 +541,7 @@ def trace_token( target_token: str, layers: list[int] | None = None, effect_threshold: float = 0.1, + progress_callback: object | None = None, ) -> CausalTraceResult: """ Trace where a target token's prediction is formed. @@ -552,8 +560,8 @@ def trace_token( if layers is None: layers = list(range(self.num_layers)) - # Get target token ID - target_id = self.tokenizer.encode(target_token) + # Get target token ID (add_special_tokens=False to avoid BOS) + target_id = self.tokenizer.encode(target_token, add_special_tokens=False) if isinstance(target_id, list): target_id = target_id[0] if target_id else 0 elif hasattr(target_id, "tolist"): @@ -583,6 +591,9 @@ def trace_token( effect = baseline_prob - ablated_prob layer_effects.append((layer_idx, effect)) + if progress_callback is not None: + progress_callback(len(layer_effects), len(layers), layer_idx) + # Find critical layers sorted_effects = sorted(layer_effects, key=lambda x: abs(x[1]), reverse=True) critical = [layer for layer, effect in sorted_effects if abs(effect) >= effect_threshold] @@ -606,6 +617,7 @@ def full_causal_trace( target_token: str, corrupt_prompt: str | None = None, layers: list[int] | None = None, + progress_callback: object | None = None, ) -> FullCausalTrace: """ Full causal tracing with position × layer grid. @@ -636,8 +648,8 @@ def full_causal_trace( tok = self.tokenizer.decode([int(input_ids[0, i])]) tokens.append(tok) - # Get target token ID - target_id = self.tokenizer.encode(target_token) + # Get target token ID (add_special_tokens=False to avoid BOS) + target_id = self.tokenizer.encode(target_token, add_special_tokens=False) if isinstance(target_id, list): target_id = target_id[0] if target_id else 0 @@ -696,6 +708,9 @@ def full_causal_trace( effects.append(tuple(pos_effects)) + if progress_callback is not None: + progress_callback(pos + 1, seq_len, len(layers)) + # Find critical positions and layers max_effects = [max(abs(e) for e in pos_effects) for pos_effects in effects] critical_positions = sorted( From 663f8be110814d17dcb2e58c417f3fde3b14ed01 Mon Sep 17 00:00:00 2001 From: chrishayuk Date: Wed, 4 Mar 2026 22:41:51 +0000 Subject: [PATCH 5/5] updated versions --- pyproject.toml | 2 +- uv.lock | 1754 +++++++++++++++++++++++++----------------------- 2 files changed, 919 insertions(+), 837 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 769b1aa2..2c316f97 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "chuk-lazarus" -version = "0.4" +version = "0.4.1" description = "MLX-based LLM training and inference with hybrid RL architecture" readme = "README.md" requires-python = ">=3.10" diff --git a/uv.lock b/uv.lock index a1a6aadc..f206fc97 100644 --- a/uv.lock +++ b/uv.lock @@ -16,6 +16,15 @@ wheels = [ { url = 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