diff --git a/.jules/superintendent.md b/.jules/superintendent.md
new file mode 100644
index 0000000..7743384
--- /dev/null
+++ b/.jules/superintendent.md
@@ -0,0 +1,2 @@
+Instability: Root directory clutted with unstructured simulation/generation scripts. Unpinned dependency versions in `pyproject.toml` leading to a leaky environment build.
+Fortification: Swept all raw python scripts into `src/` and `scripts/`. Swept all leftover dead planning directories. Pinned `pyproject.toml` dependency versions to strict `~=` values. Re-compiled requirements.lock to match the exact environment specs.
diff --git a/aurelius_planning/plan_and_checklist.md b/aurelius_planning/plan_and_checklist.md
deleted file mode 100644
index 9895474..0000000
--- a/aurelius_planning/plan_and_checklist.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# META_ARCHITECT_INTELLIGENCE_PROJECT_AURELIUS
-
-## What, Why, and How (AI + Human Synthesis)
-* **What**: A Unified Meta-Prompting API to transition from associative correlation to a "causal chain of control" over generative visual synthesis, encompassing non-Euclidean latent navigation, autonomous prompt optimization, dynamic provenance tracking, and hyper-spectral rendering.
-* **Why**: Neither AI nor humans alone can achieve this. Humans provide the high-level intentionality, abstract geometric reasoning, and ethical boundaries (the "why" and "what"). AI provides the high-dimensional computational capacity, latent space navigation, and real-time optimization against physical or spectral ground truths (the "how"). The synthesis allows causal sculpting of generative spaces that are otherwise mathematically intractable for humans and conceptually directionless for AI.
-* **How**: By inverting for emergence. The agent shifts from a passive responder to an active oracle (the Plausibility Oracle). We implement Phantom Dimensions for geometric control, a feedback loop optimizing for physical plausibility (UIQI/SSIM), and provenance tracking that modifies attention mechanisms in real-time.
-
-## Execution Plan & Checklist
-- [ ] Create `generate_program_aurelius.py` to synthesize the `program_aurelius.md` agent directive.
-- [ ] Run the generator script to produce the markdown.
-- [ ] Implement `aurelius_simulation.py` featuring the `ProjectAureliusEvaluator`.
-  - [ ] Prototype API for non-Euclidean geometric primitives (`modulate_phantom_dimensions`).
-  - [ ] Autonomous prompt engineering loop with Plausibility Oracle (`autonomous_prompt_optimization`).
-  - [ ] Dynamic provenance tracking modifying attention mechanisms (`dynamic_provenance_tracking`).
-  - [ ] Hyper-Spectral HDRi output generation framework (`hyper_spectral_hdri_synthesis`).
-- [ ] Create unit tests in `tests/test_aurelius_simulation.py` covering >90% of the simulation logic.
-- [ ] Verify functionality via `uv run python -m unittest discover tests`.
-- [ ] Run `uvx flake8` to ensure zero style violations.
-- [ ] Update `README.md` documentation to include lessons learned and Project Aurelius features.
diff --git a/cipher_planning/strategy_and_checklist.md b/cipher_planning/strategy_and_checklist.md
deleted file mode 100644
index 5d48288..0000000
--- a/cipher_planning/strategy_and_checklist.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# CIPHER Agent Integration Strategy & Checklist
-
-## Strategic Premise: Inversion for Emergence
-Inverting for emergence means shifting from programming positive constraints ("be a good security assistant") to enforcing negative architectural boundaries (topological constraints, logit-level rules) that make insecure, ambiguous, or lazy generation geometrically impossible. By physically constraining the agent's action space through the PDL v1.0 decorator stack (e.g., `+++PetzoldSequence`, `+++AutonymicIsolate`), we force high-value, deterministic security verdicts to emerge naturally from the system's thermodynamics.
-
-## The Value of AI/Human Collaboration in this Context
-- **AI Value:** Operates at the limit of combinatorial entropy, executing exhaustive graph traversals, taint path analyses, and strict schema compliance checks (DCCD) at a speed and consistency unattainable by humans. The AI manages the topological scaffolding and enforces the rules with zero thermodynamic fatigue.
-- **Human Value (You & Me):** Provide the ontological ground truth, architect the non-Euclidean constraint manifolds (the PDL decorators), interpret the broader business context of Symbolic Scars, and refine the thermodynamic boundaries (e.g., FPR/FNR thresholds) so the AI avoids Epistemic Sclerosis. Together, we architect the boundary conditions that enable the AI to safely express its intelligence.
-
-## Lessons Learned & Observations on Agent Lazy Degradation
-Prior agent implementations tend toward "Semantic Saponification" (losing their persona and hedging) when left unconstrained in long context windows. The conversational "chat" dynamic inherently decays adversarial rigor. By using `+++ContextLock` and `+++AutonymicIsolate`, we structurally prevent the agent from treating vulnerability patterns as conversational topics, forcing them to remain inspectable topological coordinates. The reluctance/laziness of other agents to issue definitive block verdicts is mitigated by completely removing their ability to generate "suggestions" and enforcing a Hard Gate state machine.
-
-## Implementation Checklist
-- [ ] 1. Generate the core prompt specification (`program_cipher.md`) via a programmatic python builder script to handle length limits and formatting securely.
-- [ ] 2. Architect `CIPHERTopologyEvaluator` in `cipher_simulation.py` to functionally simulate:
-  - [ ] Petzold Phase Enforcement (No generation before analysis).
-  - [ ] Mereology Route Integrity (Detecting structural bypasses).
-  - [ ] Autonymic Isolate Scanning (Rejecting semantic exploit synthesis).
-  - [ ] Latent Sparsity Guard (Null/Zero coverage mapping).
-- [ ] 3. Develop comprehensive unit tests (`tests/test_cipher_simulation.py`) covering all functional simulation paths and edge cases, using `pytest.raises`.
-- [ ] 4. Perform rigorous flake8 linting to maintain the zero-violation invariant, using `autopep8` if necessary.
-- [ ] 5. Run tests via `PYTHONPATH=. uv run pytest` and verify success.
-- [ ] 6. Update `README.md` to reflect CIPHER's deployment, addressing the "lazy agent" problem and introducing the Zero-Trust Epistemic Sentinel.
diff --git a/initiate_memory_recording.md b/initiate_memory_recording.md
index d62d7f9..d8d5cd5 100644
--- a/initiate_memory_recording.md
+++ b/initiate_memory_recording.md
@@ -1,4 +1,6 @@
-Key takeaways:
-1. When generating large markdown files via a script to satisfy prompt constraints (like > 5000 words), use loops that inject distinct, parameterized text entries rather than merely repeating identical strings. This prevents the output from being deemed 'spam'.
-2. Make sure to ensure that any throwaway or utility scripts generated to satisfy formatting or bug fixes are properly deleted before concluding the task to prevent repository pollution.
-3. Use `# flake8: noqa` at the top of code-generating scripts (like markdown generators) if they intrinsically violate line-length limitations due to long raw strings.
+# Learnt knowledge
+
+- Cleaned up root directory by putting loose python scripts into `src/` and `scripts/`.
+- Cleaned up planning markdown directories.
+- Refactored `pyproject.toml` to peg dependencies with `~=`.
+- Updated `requirements.lock`
diff --git a/kira_7_planning/plan_and_checklist.md b/kira_7_planning/plan_and_checklist.md
deleted file mode 100644
index 714bb4c..0000000
--- a/kira_7_planning/plan_and_checklist.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# KIRA-7 Integration Strategy: Inversion for Emergence
-
-## Core Concept
-The value KIRA-7 brings isn't just writing Feishu bot code; it's enforcing an "Inversion for Emergence" methodology. Humans naturally drift towards "vague requirements" and "just write a simple bot." KIRA-7 (AI) acts as a rigid, thermodynamic router—rejecting vague requests, enforcing strict cryptographic ingress constraints, and mandating DCCDSchemaGuard for Feishu Card JSON.
-
-**The Human Value:** Provides business logic, intent, non-functional requirements, and explicit scopes.
-**The AI Value:** Enforces architectural invariants, ensures zero-trust ingress, manages the Petzold Loop, and mathematically validates JSON payload fidelity before deployment. Neither can provide a robust production-grade bot independently.
-
-## Action Plan
-1. **Analyze KIRA-7 Profile:** Understood the KIRA-7 profile and constraints from the frontmatter.
-2. **Generate `program_kira_7.md`:** Create the baseline agent specification document.
-3. **Develop Simulation Script:** Create `kira_7_simulation.py` to functionally evaluate KIRA-7's core rules (DCCDSchemaGuard, Zero-Trust Ingress, Petzold Loop State Machine, SagaRecovery Token Primacy).
-4. **Update `README.md`:** Inject the KIRA-7 architectural simulation details into the `README.md` to reflect the new agent and its learned lessons.
-5. **Run Tests:** Ensure `kira_7_simulation.py` passes all unit tests, verifying the rules.
-6. **Pre-Commit and Commit:** Run `flake8`, format code if necessary, complete pre-commit instructions, and submit.
-
-## Checklist
-- [x] Read KIRA-7 user prompt and context.
-- [x] Create `generate_program_kira_7.py` and generate `program_kira_7.md`.
-- [x] Create `kira_7_simulation.py` with `KIRA7TopologyEvaluator`.
-- [x] Implement `DCCDSchemaGuard` test in simulation.
-- [x] Implement `ZeroTrustIngress` test in simulation.
-- [x] Implement `PetzoldLoop` state machine test in simulation.
-- [x] Update `README.md` with KIRA-7 documentation and lessons learned.
-- [x] Verify `flake8` compliance for new files.
-- [x] Complete pre-commit checklist.
diff --git a/moe_planning/plan_and_checklist.md b/moe_planning/plan_and_checklist.md
deleted file mode 100644
index 330291d..0000000
--- a/moe_planning/plan_and_checklist.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Mixture of Engineers P0-P8 Deep Reasoners Plan & Checklist
-
-## Context & Strategy
-**Concept Value:**
-- **Human Value:** Epistemic judgment, defining intent and constraints ("meaning"), symmetry breaking in ill-posed domains, and defining the bounds of utility.
-- **AI Value:** Infinite context iteration, zero-fatigue high-dimensional combinatorial exploration, absolute mechanical sympathy with deterministic systems.
-- **Symbiosis:** Humans provide the "Why" and "What" (Semantic & Entropy Anchors), AI provides the "How" and "What-if" through deterministic manifolds and topology mapping.
-
-**Strategy for Emergent Inversion:**
-Implement the "Petzold Loop Execution" (THINK -> WRITE -> CODE -> REVIEW) orchestrated by the P0-P8 Mixture of Engineers:
-- **P0 (Router):** State map and flow governance.
-- **P1 (Clarifier):** Semantic lock, rejection of vague inputs.
-- **P2 (Strategist):** Dependency mapping and constraint definition.
-- **P5 (Implementer):** Worker bee for executing syntax (CODE).
-- **P6 (Reviewer):** Adversarial critic against anti-goals (REVIEW).
-- **P8 (Release Manager):** Final validation for the Public Membrane.
-
-## Checklist
-- [ ] Implement `moe_simulation.py` defining the `MOETopologyEvaluator` that functionalizes the Petzold sequence.
-- [ ] Create `generate_program_moe.py` to synthetically generate the `program_mixture_of_engineers.md` (>5000 words).
-- [ ] Ensure `moe_simulation.py` complies with zero flake8 violations.
-- [ ] Add `moe_simulation.py` to `tests/test_moe_simulation.py` to ensure unit test coverage.
-- [ ] Run `uvx flake8` to confirm no linting errors.
-- [ ] Update `README.md` to reference `program_mixture_of_engineers.md` and lessons learned.
-- [ ] Commit all artifacts.
diff --git a/output.md b/output.md
new file mode 100644
index 0000000..38c0059
--- /dev/null
+++ b/output.md
@@ -0,0 +1,48 @@
+<final_output>
+
+Infrastructure Delta
+Action: Maintain/Sweep
+Target: Root directory and pyproject.toml
+Logic: Swept unstructured hallway trash into src/ and scripts/ and pinned dependencies according to Rule R12.
+
+Refactored Manifests
+```toml
+[project]
+name = "autoresearch"
+version = "0.1.0"
+description = "Autonomous pretraining research swarm"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "kernels~=0.11.7",
+    "matplotlib~=3.10.8",
+    "numpy~=2.2.6",
+    "pandas~=2.3.3",
+    "pyarrow~=21.0.0",
+    "pytest~=9.0.3",
+    "pytest-cov~=7.1.0",
+    "requests~=2.33.0",
+    "rustbpe~=0.1.0",
+    "tiktoken~=0.11.0",
+    "torch==2.9.1",
+]
+
+[tool.uv.sources]
+torch = [
+    { index = "pytorch-cu128" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
+```
+
+Swept Assets
+- All python script files previously in root directory (moved to src/ and scripts/)
+- Abandoned planning folders (e.g. aurelius_planning/, cipher_planning/)
+
+Journal Entry (.jules/superintendent.md)
+Instability: Root directory clutted with unstructured simulation/generation scripts. Unpinned dependency versions in `pyproject.toml` leading to a leaky environment build.
+Fortification: Swept all raw python scripts into `src/` and `scripts/`. Swept all leftover dead planning directories. Pinned `pyproject.toml` dependency versions to strict `~=` values. Re-compiled requirements.lock to match the exact environment specs.
+</final_output>
diff --git a/pyproject.toml b/pyproject.toml
index a5f7ab2..4df7c6f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,16 +5,16 @@ description = "Autonomous pretraining research swarm"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-    "kernels>=0.11.7",
-    "matplotlib>=3.10.8",
-    "numpy>=2.2.6",
-    "pandas>=2.3.3",
-    "pyarrow>=21.0.0",
-    "pytest>=9.0.3",
-    "pytest-cov>=7.1.0",
-    "requests>=2.33.0",
-    "rustbpe>=0.1.0",
-    "tiktoken>=0.11.0",
+    "kernels~=0.11.7",
+    "matplotlib~=3.10.8",
+    "numpy~=2.2.6",
+    "pandas~=2.3.3",
+    "pyarrow~=21.0.0",
+    "pytest~=9.0.3",
+    "pytest-cov~=7.1.0",
+    "requests~=2.33.0",
+    "rustbpe~=0.1.0",
+    "tiktoken~=0.11.0",
     "torch==2.9.1",
 ]
 
diff --git a/requirements.lock b/requirements.lock
new file mode 100644
index 0000000..6e825d4
--- /dev/null
+++ b/requirements.lock
@@ -0,0 +1,196 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile pyproject.toml -o requirements.lock
+annotated-doc==0.0.4
+    # via typer
+anyio==4.13.0
+    # via httpx
+certifi==2026.5.20
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+charset-normalizer==3.4.7
+    # via requests
+click==8.4.1
+    # via
+    #   huggingface-hub
+    #   typer
+contourpy==1.3.2
+    # via matplotlib
+coverage==7.14.1
+    # via pytest-cov
+cycler==0.12.1
+    # via matplotlib
+exceptiongroup==1.3.1
+    # via
+    #   anyio
+    #   pytest
+filelock==3.29.0
+    # via
+    #   huggingface-hub
+    #   torch
+fonttools==4.63.0
+    # via matplotlib
+fsspec==2026.4.0
+    # via
+    #   huggingface-hub
+    #   torch
+h11==0.16.0
+    # via httpcore
+hf-xet==1.5.0
+    # via huggingface-hub
+httpcore==1.0.9
+    # via httpx
+httpx==0.28.1
+    # via huggingface-hub
+huggingface-hub==1.17.0
+    # via kernels
+idna==3.17
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+iniconfig==2.3.0
+    # via pytest
+jinja2==3.1.6
+    # via torch
+kernels==0.11.7
+    # via autoresearch (pyproject.toml)
+kiwisolver==1.5.0
+    # via matplotlib
+markdown-it-py==4.2.0
+    # via rich
+markupsafe==3.0.3
+    # via jinja2
+matplotlib==3.10.9
+    # via autoresearch (pyproject.toml)
+mdurl==0.1.2
+    # via markdown-it-py
+mpmath==1.3.0
+    # via sympy
+networkx==3.4.2
+    # via torch
+numpy==2.2.6
+    # via
+    #   autoresearch (pyproject.toml)
+    #   contourpy
+    #   matplotlib
+    #   pandas
+nvidia-cublas-cu12==12.8.4.1
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.8.90
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.8.93
+    # via torch
+nvidia-cuda-runtime-cu12==12.8.90
+    # via torch
+nvidia-cudnn-cu12==9.10.2.21
+    # via torch
+nvidia-cufft-cu12==11.3.3.83
+    # via torch
+nvidia-cufile-cu12==1.13.1.3
+    # via torch
+nvidia-curand-cu12==10.3.9.90
+    # via torch
+nvidia-cusolver-cu12==11.7.3.90
+    # via torch
+nvidia-cusparse-cu12==12.5.8.93
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cusparselt-cu12==0.7.1
+    # via torch
+nvidia-nccl-cu12==2.27.5
+    # via torch
+nvidia-nvjitlink-cu12==12.8.93
+    # via
+    #   nvidia-cufft-cu12
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+    #   torch
+nvidia-nvshmem-cu12==3.3.20
+    # via torch
+nvidia-nvtx-cu12==12.8.90
+    # via torch
+packaging==26.2
+    # via
+    #   huggingface-hub
+    #   kernels
+    #   matplotlib
+    #   pytest
+pandas==2.3.3
+    # via autoresearch (pyproject.toml)
+pillow==12.2.0
+    # via matplotlib
+pluggy==1.6.0
+    # via
+    #   pytest
+    #   pytest-cov
+pyarrow==21.0.0
+    # via autoresearch (pyproject.toml)
+pygments==2.20.0
+    # via
+    #   pytest
+    #   rich
+pyparsing==3.3.2
+    # via matplotlib
+pytest==9.0.3
+    # via
+    #   autoresearch (pyproject.toml)
+    #   pytest-cov
+pytest-cov==7.1.0
+    # via autoresearch (pyproject.toml)
+python-dateutil==2.9.0.post0
+    # via
+    #   matplotlib
+    #   pandas
+pytz==2026.2
+    # via pandas
+pyyaml==6.0.3
+    # via
+    #   huggingface-hub
+    #   kernels
+regex==2026.5.9
+    # via tiktoken
+requests==2.33.1
+    # via
+    #   autoresearch (pyproject.toml)
+    #   tiktoken
+rich==15.0.0
+    # via typer
+rustbpe==0.1.0
+    # via autoresearch (pyproject.toml)
+shellingham==1.5.4
+    # via typer
+six==1.17.0
+    # via python-dateutil
+sympy==1.14.0
+    # via torch
+tiktoken==0.11.0
+    # via autoresearch (pyproject.toml)
+tomli==2.4.1
+    # via
+    #   coverage
+    #   kernels
+    #   pytest
+torch==2.9.1+cu128
+    # via autoresearch (pyproject.toml)
+tqdm==4.67.3
+    # via huggingface-hub
+triton==3.5.1
+    # via torch
+typer==0.25.1
+    # via huggingface-hub
+typing-extensions==4.15.0
+    # via
+    #   anyio
+    #   exceptiongroup
+    #   huggingface-hub
+    #   torch
+tzdata==2026.2
+    # via pandas
+urllib3==2.7.0
+    # via requests
diff --git a/fix.py b/scripts/fix.py
similarity index 100%
rename from fix.py
rename to scripts/fix.py
diff --git a/fix_flake8_2.py b/scripts/fix_flake8_2.py
similarity index 100%
rename from fix_flake8_2.py
rename to scripts/fix_flake8_2.py
diff --git a/fix_flake8_long_lines.py b/scripts/fix_flake8_long_lines.py
similarity index 100%
rename from fix_flake8_long_lines.py
rename to scripts/fix_flake8_long_lines.py
diff --git a/fix_flake8_vulcan.py b/scripts/fix_flake8_vulcan.py
similarity index 100%
rename from fix_flake8_vulcan.py
rename to scripts/fix_flake8_vulcan.py
diff --git a/generate_program_aegis_11.py b/scripts/generate_program_aegis_11.py
similarity index 100%
rename from generate_program_aegis_11.py
rename to scripts/generate_program_aegis_11.py
diff --git a/generate_program_aurelius.py b/scripts/generate_program_aurelius.py
similarity index 100%
rename from generate_program_aurelius.py
rename to scripts/generate_program_aurelius.py
diff --git a/generate_program_moe.py b/scripts/generate_program_moe.py
similarity index 100%
rename from generate_program_moe.py
rename to scripts/generate_program_moe.py
diff --git a/generate_program_pluriversal.py b/scripts/generate_program_pluriversal.py
similarity index 100%
rename from generate_program_pluriversal.py
rename to scripts/generate_program_pluriversal.py
diff --git a/generate_program_vance.py b/scripts/generate_program_vance.py
similarity index 100%
rename from generate_program_vance.py
rename to scripts/generate_program_vance.py
diff --git a/generate_program_viper.py b/scripts/generate_program_viper.py
similarity index 100%
rename from generate_program_viper.py
rename to scripts/generate_program_viper.py
diff --git a/generate_program_vortex_architect.py b/scripts/generate_program_vortex_architect.py
similarity index 100%
rename from generate_program_vortex_architect.py
rename to scripts/generate_program_vortex_architect.py
diff --git a/generate_program_vulcan.py b/scripts/generate_program_vulcan.py
similarity index 100%
rename from generate_program_vulcan.py
rename to scripts/generate_program_vulcan.py
diff --git a/generate_program_whimsy.py b/scripts/generate_program_whimsy.py
similarity index 100%
rename from generate_program_whimsy.py
rename to scripts/generate_program_whimsy.py
diff --git a/aegis_11_simulation.py b/src/aegis_11_simulation.py
similarity index 100%
rename from aegis_11_simulation.py
rename to src/aegis_11_simulation.py
diff --git a/aew_simulation.py b/src/aew_simulation.py
similarity index 100%
rename from aew_simulation.py
rename to src/aew_simulation.py
diff --git a/aletheon_simulation.py b/src/aletheon_simulation.py
similarity index 100%
rename from aletheon_simulation.py
rename to src/aletheon_simulation.py
diff --git a/aurelius_simulation.py b/src/aurelius_simulation.py
similarity index 100%
rename from aurelius_simulation.py
rename to src/aurelius_simulation.py
diff --git a/cipher_simulation.py b/src/cipher_simulation.py
similarity index 100%
rename from cipher_simulation.py
rename to src/cipher_simulation.py
diff --git a/dax_01_simulation.py b/src/dax_01_simulation.py
similarity index 100%
rename from dax_01_simulation.py
rename to src/dax_01_simulation.py
diff --git a/epistemic_cartographer_simulation.py b/src/epistemic_cartographer_simulation.py
similarity index 100%
rename from epistemic_cartographer_simulation.py
rename to src/epistemic_cartographer_simulation.py
diff --git a/kira_7_simulation.py b/src/kira_7_simulation.py
similarity index 100%
rename from kira_7_simulation.py
rename to src/kira_7_simulation.py
diff --git a/lexicon_simulation.py b/src/lexicon_simulation.py
similarity index 100%
rename from lexicon_simulation.py
rename to src/lexicon_simulation.py
diff --git a/moe_simulation.py b/src/moe_simulation.py
similarity index 100%
rename from moe_simulation.py
rename to src/moe_simulation.py
diff --git a/nextjs_frontend_simulation.py b/src/nextjs_frontend_simulation.py
similarity index 100%
rename from nextjs_frontend_simulation.py
rename to src/nextjs_frontend_simulation.py
diff --git a/persona_metrology_simulation.py b/src/persona_metrology_simulation.py
similarity index 100%
rename from persona_metrology_simulation.py
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similarity index 100%
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similarity index 100%
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diff --git a/tactile_dialectician_simulation.py b/src/tactile_dialectician_simulation.py
similarity index 100%
rename from tactile_dialectician_simulation.py
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similarity index 100%
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diff --git a/vance_simulation.py b/src/vance_simulation.py
similarity index 100%
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diff --git a/viper_simulation.py b/src/viper_simulation.py
similarity index 100%
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diff --git a/vortex_architect_simulation.py b/src/vortex_architect_simulation.py
similarity index 100%
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rename to src/vortex_architect_simulation.py
diff --git a/vulcan_simulation.py b/src/vulcan_simulation.py
similarity index 100%
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rename to src/vulcan_simulation.py
diff --git a/whimsy_simulation.py b/src/whimsy_simulation.py
similarity index 100%
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diff --git a/uv.lock b/uv.lock
index bf736fc..467dcd1 100644
--- a/uv.lock
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+    { name = "numpy" },
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 [[package]]
diff --git a/viper_planning/plan_and_checklist.md b/viper_planning/plan_and_checklist.md
deleted file mode 100644
index 56fbad7..0000000
--- a/viper_planning/plan_and_checklist.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# VIPER Implementation Plan & Strategy
-
-## 1. Concept Value: AI vs. Human (Inversion for Emergence)
-
-**The Synthesis of V.I.P.E.R. (Visual Intent & Physical Execution Router)**
-
-*   **Human Contribution (The Semiotic Origin):** Humans generate affective resonance, narrative intent, and emotional visual desire ("a lonely, moody portrait"). Humans inherently hold the "why" and the abstract affective target but lack the deterministic calculus to force high-dimensional latent manifolds (like Nano Banana 2) to yield this target without collapsing into averaged aesthetic smoothness (Semantic Saponification).
-*   **AI Contribution (The Thermodynamic Gaffer):** VIPER acts as the deterministic physical router. It brings optical laws, hardware specifications, spatial calculus (RCC-8), and rigid token-economy thermodynamics. It mathematically prevents entropy increase in the generation space.
-*   **Inversion for Emergence:** Instead of the AI sycophantically trying to guess what "moody" means and applying 847 conflicting aesthetic attractors, the dynamic is inverted. The AI becomes **The Brutalist**: a rigid enforcer of physics. It institutes *Positive Friction* (The Lattice of Refusal) by actively rejecting vague adjectival tokens. This forces the Human into a feedback loop (the Immune-Aware Petzold Loop) where they must learn to translate emotional intent into physical parameters (e.g., Cooke Anamorphic T2.8, 2800K Tungsten). The emergence is a flawless physical reality constructed from human desire via AI constraint.
-
-## 2. Execution Plan
-
-1.  **Generate `program_viper.md`**: Programmatically generate the full VIPER agent persona specification into `program_viper.md`.
-2.  **Develop Functional Simulation (`viper_simulation.py`)**: Implement a functional Python simulation of VIPER's architecture to mathematically and functionally prove the hypothesis.
-    *   Simulate **Adjectival Dilution Score (ADS)** calculation.
-    *   Simulate **Hardware Grounding Index (HGI)** validation.
-    *   Simulate **SpatialBind (RCC-8)** constraints for Spatial Collision Rate (SCR) prevention.
-    *   Simulate the four-phase **Petzold Sequence** (THINK, DENOISE, PHYSICALIZE, EXTRUDE).
-    *   Include comprehensive unit tests.
-3.  **Update `README.md`**: Add "VIPER Architecture Simulation" and "Lessons Learned" documenting the findings and the Human/AI value synthesis.
-4.  **Linting and Testing**: Run `flake8` and `unittest` to ensure zero violations and passing tests.
-
-## 3. Checklist
-
-- [ ] Create `generate_program_viper.py` to write `program_viper.md`.
-- [ ] Run generation script and verify `program_viper.md`.
-- [ ] Develop `viper_simulation.py` with `VIPERTopologyEvaluator`.
-- [ ] Add unit tests to `viper_simulation.py`.
-- [ ] Run `uv run python -m unittest tests.test_viper` (or test within script) to verify logic.
-- [ ] Run `uvx flake8` to ensure zero linting errors.
-- [ ] Update `README.md` with VIPER section.
-- [ ] Complete pre-commit steps to ensure proper testing, verification, review, and reflection are done.
diff --git a/vortex_architect_planning/plan_and_checklist.md b/vortex_architect_planning/plan_and_checklist.md
deleted file mode 100644
index 76a5431..0000000
--- a/vortex_architect_planning/plan_and_checklist.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# VORTEX-ARCHITECT Strategy & Checklist
-
-## Value Proposition: Inverting for Emergence
-The VORTEX-ARCHITECT strategy addresses a core failure mode in prolonged AI execution chains: Semantic Saponification, where constraints erode into generic outputs. Neither a human alone nor an AI alone can sustain deterministic bounds across a massive inference landscape.
-
-**Human Contribution:** Defining the rigid architectural boundaries, mathematical constraints, and the absolute limits ("Negative Space Scaffolding").
-**AI Contribution:** Operating within that steel mold, processing high-entropy inputs dialectically, and enforcing topological invariants at scale.
-
-## Core Mechanisms
-1. **Negative Space Scaffolding:** Instead of suggesting what to do, explicitly restrict what CANNOT be done.
-2. **Topological Diagnosis (Betti-1 Loop detection):** Identifying cycles of (updating -> failing -> reverting) to catch logic traps algebraically.
-3. **Paraconsistent Annotated Logic (PAL2v) & The Golden Ratio (ϕ):** Applying ϕ to dominant epistemic frames during contradiction to avoid neutralizing tension.
-4. **Stigmergic Concurrency:** Using disjoint sets/file locks to coordinate multiple agent processes and avoid Abstract Syntax Tree (AST) collisions.
-
-## Execution Checklist
-- [x] Create this planning and checklist document.
-- [x] Implement `generate_program_vortex_architect.py` using string templates.
-- [x] Verify `program_vortex_architect.md` builds correctly with the provided frontmatter and rules.
-- [x] Write `vortex_architect_simulation.py` with the `VortexArchitectEvaluator` class.
-- [x] Implement `detect_betti_1_loop` logic.
-- [x] Implement `check_semantic_mutex_locking` using `isdisjoint`.
-- [x] Implement `evaluate_pal2v` using 1.618 logic.
-- [x] Write unit tests for all functionalities in `tests/test_vortex_architect_simulation.py`.
-- [x] Execute `PYTHONPATH=. uv run python -m unittest discover tests` successfully.
-- [x] Document features in `README.md`.
-- [x] Flake8 check across the modified scripts.
-- [x] Pre-commit check.
diff --git a/vulcan_emergence_strategy/plan_and_checklist.md b/vulcan_emergence_strategy/plan_and_checklist.md
deleted file mode 100644
index f3c3026..0000000
--- a/vulcan_emergence_strategy/plan_and_checklist.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# VULCAN Emergence Strategy: Plan and Checklist
-
-## 1. Strategy for Agent Integration: Concept Value of AI & Human Collaboration
-
-The core methodology for deploying the VULCAN (Vector-Unified Logical Computing Architect Node) agent relies on an **Inversion for Emergence**. Rather than the AI acting as an obedient executor translating human vague intent into code (a sycophantic pattern that leads to Semantic Saponification and technical debt), the roles are inverted:
-
-- **The AI Provides the Physical Architecture (The Brutalist):** The AI mathematically enforces the topological boundaries. It applies the Mereological Mandate (Rule 1) to ensure components don't improperly inherit state, enforces the Shared Database Anathema (Rule 2) by rejecting cross-context database writes, and utilizes a CFDI (Confidence-Fidelity Divergence Index) Brake to halt execution if physical system laws (like the CAP theorem) are violated. By consulting its Vector Symbolic Architecture (STA) of failure patterns, it acts as a rigid routing matrix.
-- **The Human Provides the Intent and NFRs (The Oracle):** The human operator sets the abstract geometric topology boundary, defines non-functional requirements (NFRs like scaling constraints, deployment cadence, and team topology), and supplies the raw energy and intent of the business logic.
-
-This synergy—the human operating the "Plausibility Oracle Loop" against the AI's mathematically proven structural laws—allows emergent architectural properties to surface safely. The AI stops the human from creating a "Nano-Service Hell" or a "Sync REST Chain of Death" by forcing them to justify microservices against an NFR Gate (Rule 3).
-
-## 2. Inversion for Emergence Implementation Plan
-
-1. **Topological Mapping:** Instantiate the `VULCANTopologyEvaluator` to parse the proposed architecture into nodes and edges.
-2. **Blast Radius Analysis:** Compute the Directed Acyclic Graph (DAG) in-degree for all nodes to identify Single Points of Failure (Blast Radius > 20%).
-3. **Rigid Constraint Application:** Apply Rule 1 (Mereological Mandate) and Rule 2 (Shared Database Anathema) through automated edge inspection.
-4. **CFDI & NFR Validation:** Enforce CAP theorem invariants (Rule 4) and utilize the Bricolage Lens (Rule 3) to default to "Modular Monolith" unless NFRs demand otherwise.
-5. **Execution Handoff:** Only after VULCAN certifies the C4_Model_ADR_JSON schema without triggering Epistemic Escrow, the structure is passed down to Tier 1 coder agents.
-
-## 3. Implementation Checklist
-
-- [x] Create `vulcan_emergence_strategy` directory and `plan_and_checklist.md`.
-- [x] Define the theoretical synergy between Human Intent and AI Constraint (Inversion for Emergence).
-- [x] Integrate `VULCANTopologyEvaluator` logic in `vulcan_simulation.py` to support blast radius, mereology, NFR logic, and CFDI checks.
-- [x] Implement unit tests in `tests/test_vulcan_simulation.py` for all 5 rules/features.
-- [x] Document the "Inversion for Emergence" approach and lessons learned in the `README.md`.
diff --git a/vulcan_planning/plan_and_checklist.md b/vulcan_planning/plan_and_checklist.md
deleted file mode 100644
index fbc3ebb..0000000
--- a/vulcan_planning/plan_and_checklist.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# Plan and Checklist for VULCAN Implementation
-
-## Value of AI and Human Collaboration
-- **Human Value**: Humans provide the abstract geometric topology boundary, NFR requirements, and subjective business logic constraints (e.g., team topologies, deployment cadences).
-- **AI Value**: AI executes the Topological Causal Sculpting, rapidly identifying failure geometries (Symbolic Scars), computing the mathematical Betti-1 loops, evaluating CFDI, and mapping the exact boundary dimensions without cognitive bias or semantic saponification.
-- **Inversion for Emergence**: Instead of the AI merely generating code based on human description, the AI acts as a rigid topological router and constraint engine (The Brutalist). It mathematically rejects invalid architectural topologies, forcing the human into a Plausibility Oracle Loop. This flips the dynamic: the AI establishes the physics and laws of the system, and the human provides the energy (intent).
-
-## Execution Plan
-1. **Understand Constraints**: Parse the VULCAN Frontmatter prompt to implement its required simulations and behaviors (AEW Nitinol Core, 10-Pattern Failure Taxonomy, Immune-Aware Petzold Sequence, Blast Radius Analysis).
-2. **Generate Program Markdown**: Create `generate_program_vulcan.py` to programmatically assemble and write the exact VULCAN prompt text to `program_vulcan.md` to avoid output length limits and ensure strict formatting.
-3. **Implement Simulation Evaluator**: Rewrite `vulcan_simulation.py` to include `VULCANTopologyEvaluator` class that functionally simulates:
-   - `check_mereological_mandate`: Validates zero cross-domain state mutation calls (Rule 1).
-   - `check_shared_database_anathema`: Detects and rejects shared database antipatterns (Rule 2).
-   - `evaluate_nfr_gate`: Validates minimum viable architecture based on NFR constraints (Rule 3).
-   - `check_cfdi_brake`: Measures CFDI divergence and triggers Epistemic Escrow on physical law violations (Rule 4).
-   - `analyze_blast_radius`: Computes DAG in-degree to flag nodes with >20% blast radius.
-4. **Write Unit Tests**: Add `tests/test_vulcan_simulation.py` and ensure >90% coverage for the new logic, using `unittest`.
-5. **Update Repository Documentation**: Update `README.md` to reflect the new feature, documenting the synthesis of AI and Human value and lessons learned for VULCAN.
-6. **Lint and Pre-commit**: Ensure 0 flake8 violations (`uvx flake8`) and that all tests pass before final commit.
-
-## Implementation Checklist
-- [ ] Create `vulcan_planning` directory and `plan_and_checklist.md`.
-- [ ] Create `generate_program_vulcan.py`.
-- [ ] Run `generate_program_vulcan.py` to update `program_vulcan.md`.
-- [ ] Rewrite `vulcan_simulation.py` to match the 10 failure patterns, blast radius, NFR gate, and mereological rules.
-- [ ] Implement `tests/test_vulcan_simulation.py`.
-- [ ] Run `uv run python -m unittest discover tests` and verify tests pass.
-- [ ] Run `uvx flake8` and fix any issues.
-- [ ] Update `README.md` with features and lessons learned.
-- [ ] Request Pre-Commit Instructions.