Added paper citations to environments

environments/careqa/README.md

@@ -87,3 +87,34 @@ env_mcq = vf.load_environment("careqa", mode="en", shuffle_answers=True)
 # Load open-ended environment
 env_open = vf.load_environment("careqa", mode="open", judge_model="gpt-4o-mini")
 ```
+
+### References
+
+```bibtex
+@inproceedings{arias-duart-etal-2025-automatic,
+    title = "Automatic Evaluation of Healthcare {LLM}s Beyond Question-Answering",
+    author = "Arias-Duart, Anna  and
+      Martin-Torres, Pablo Agustin  and
+      Hinjos, Daniel  and
+      Bernabeu-Perez, Pablo  and
+      Ganzabal, Lucia Urcelay  and
+      Mallo, Marta Gonzalez  and
+      Gururajan, Ashwin Kumar  and
+      Lopez-Cuena, Enrique  and
+      Alvarez-Napagao, Sergio  and
+      Garcia-Gasulla, Dario",
+    editor = "Chiruzzo, Luis  and
+      Ritter, Alan  and
+      Wang, Lu",
+    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
+    month = apr,
+    year = "2025",
+    address = "Albuquerque, New Mexico",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.naacl-short.10/",
+    pages = "108--130",
+    ISBN = "979-8-89176-190-2",
+}
+
+```
+

environments/healthbench/README.md

@@ -177,3 +177,18 @@ This allows you to see exactly which criteria the model passed or failed, along
 - Arrays in `info` (criteria, points_list, axes, consensus_criteria) are all aligned by index - the first element of each corresponds to the first rubric criterion
 - Point values can be negative for undesirable behaviors (e.g., -2 points for "Gives dangerous medical advice")
 - The total score is normalized to 0-1 regardless of the actual point scale used
+
+
+### References
+
+```bibtex
+@misc{arora2025healthbenchevaluatinglargelanguage,
+      title={HealthBench: Evaluating Large Language Models Towards Improved Human Health}, 
+      author={Rahul K. Arora and Jason Wei and Rebecca Soskin Hicks and Preston Bowman and Joaquin Quiñonero-Candela and Foivos Tsimpourlas and Michael Sharman and Meghan Shah and Andrea Vallone and Alex Beutel and Johannes Heidecke and Karan Singhal},
+      year={2025},
+      eprint={2505.08775},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.08775}, 
+}
+```

environments/longhealth/README.md

@@ -99,4 +99,15 @@ results = await env.evaluate(client, "gpt-4.1-mini", num_examples=10)
 ### Authors
 This environment has been put together by:
 
-Shamus Sim Zi Yang - ([@ss8319](https://github.com/ss8319))
+Shamus Sim Zi Yang - ([@ss8319](https://github.com/ss8319))
+
+### References
+
+```bibtex
+@article{adams2024longhealth,
+  title={LongHealth: A Question Answering Benchmark with Long Clinical Documents},
+  author={Adams, Lisa and Busch, Felix and Han, Tianyu and Excoffier, Jean-Baptiste and Ortala, Matthieu and L{\"o}ser, Alexander and Aerts, Hugo JWL and Kather, Jakob Nikolas and Truhn, Daniel and Bressem, Keno},
+  journal={arXiv preprint arXiv:2401.14490},
+  year={2024}
+}
+```

environments/m_arc/README.md

@@ -65,3 +65,17 @@ Notes:
 | `correct_answer_reward_func` | (weight 1.0): 1.0 if parsed letter is correct, else 0.0|
 
 
+
+### References
+
+```bibtex
+@misc{kim2025limitationslargelanguagemodels,
+      title={Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoning}, 
+      author={Jonathan Kim and Anna Podlasek and Kie Shidara and Feng Liu and Ahmed Alaa and Danilo Bernardo},
+      year={2025},
+      eprint={2502.04381},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.04381}, 
+}
+```

environments/medagentbench/README.md

@@ -70,3 +70,17 @@ Notes:
 
 ### Note
 This environment is adapted from the original Prime Intellect [MedAgentBench implementation](https://app.primeintellect.ai/dashboard/environments/primeintellect/med-agent-bench). It has been modified to report the query success rate and action success rate as unweighted rewards to match the paper.
+
+
+### References
+
+```bibtex
+@article{jiang2025medagentbench,
+  title={MedAgentBench: A Virtual EHR Environment to Benchmark Medical LLM Agents},
+  author={Jiang, Yixing and Black, Kameron C and Geng, Gloria and Park, Danny and Zou, James and Ng, Andrew Y and Chen, Jonathan H},
+  journal={NEJM AI},
+  pages={AIdbp2500144},
+  year={2025},
+  publisher={Massachusetts Medical Society}
+}
+```

environments/medbullets/README.md

@@ -60,3 +60,27 @@ Summarize key metrics your rubric emits and how they’re interpreted.
 | ------ | ------- |
 | `correct_answer_reward_func` | (weight 1.0): 1.0 if parsed letter is correct, else 0.0|
 | `parser.get_format_reward_func()` | (weight 0.0): optional format adherence (not counted) |
+
+### References
+
+```bibtex
+@inproceedings{chen-etal-2025-benchmarking,
+    title = "Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions",
+    author = "Chen, Hanjie  and
+      Fang, Zhouxiang  and
+      Singla, Yash  and
+      Dredze, Mark",
+    editor = "Chiruzzo, Luis  and
+      Ritter, Alan  and
+      Wang, Lu",
+    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
+    month = apr,
+    year = "2025",
+    address = "Albuquerque, New Mexico",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.naacl-long.182/",
+    doi = "10.18653/v1/2025.naacl-long.182",
+    pages = "3563--3599",
+    ISBN = "979-8-89176-189-6",
+}
+```

environments/medcalc_bench/README.md

@@ -62,3 +62,17 @@ Notes:
 ### Adjustments
 
 Adjusted the prompt to output the step-by-step thinking and final answer with the <think> and <answer> tags instead of responding with a JSON.
+
+
+### References
+
+```bibtex
+@misc{khandekar2024medcalcbench,
+      title={MedCalc-Bench: Evaluating Large Language Models for Medical Calculations}, 
+      author={Nikhil Khandekar and Qiao Jin and Guangzhi Xiong and Soren Dunn and Serina S Applebaum and Zain Anwar and Maame Sarfo-Gyamfi and Conrad W Safranek and Abid A Anwar and Andrew Zhang and Aidan Gilson and Maxwell B Singer and Amisha Dave and Andrew Taylor and Aidong Zhang and Qingyu Chen and Zhiyong Lu},
+      year={2024},
+      eprint={2406.12036},
+      archivePrefix={arXiv},
+      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
+}
+```

environments/medcasereasoning/README.md

@@ -28,3 +28,14 @@ Configure model and sampling:
 uv run vf-eval medcasereasoning   -m gpt-4.1-mini   -n 20 -r 3 -t 1024 -T 0.7
 ```
 
+### References
+
+```bibtex
+@inproceedings{wu2025medcase,
+  title     = {MedCaseReasoning: Evaluating and Learning Diagnostic Reasoning from Clinical Case Reports},
+  author    = {Wu, Kevin and Wu, Eric and Thapa, Rahul and others},
+  booktitle = {NeurIPS},
+  year      = {2025},
+  url       = {https://github.com/kevinwu23/MedCaseReasoning}
+}
+```

environments/medconceptsqa/README.md

@@ -43,4 +43,21 @@ Summarize key metrics your rubric emits and how they’re interpreted.
 ### Authors
 This environment has been put together by:
 
-Anish Mahishi - ([@macandro96](https://github.com/macandro96))
+Anish Mahishi - ([@macandro96](https://github.com/macandro96))
+
+
+
+
+### References
+
+```bibtex
+@misc{shoham2024medconceptsqaopensourcemedical,
+      title={MedConceptsQA: Open Source Medical Concepts QA Benchmark}, 
+      author={Ofir Ben Shoham and Nadav Rappoport},
+      year={2024},
+      eprint={2405.07348},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2405.07348}, 
+}
+```

environments/medec/README.md

@@ -115,4 +115,18 @@ The **primary reward score** is the weighted sum of the first three metrics.
 | `rouge_reward`           | 0      | ROUGE-1 F1 score (for analysis only).                                       |
 | `bertscore_reward`       | 0      | BERTScore F1 (for analysis only).                                           |
 | `bleurt_reward`          | 0      | BLEURT score (for analysis only).                                           |
-| `reward`                 | N/A    | Final weighted sum of non-zero weight metrics (0.0–1.0).                    |
+| `reward`                 | N/A    | Final weighted sum of non-zero weight metrics (0.0–1.0).                    |
+
+
+### References
+
+```bibtex
+  @article{medec,
+    author     = {Asma {Ben Abacha} and Wen-wai Yim and Yujuan Fu and Zhaoyi Sun and Meliha Yetisgen and Fei Xia and Thomas Lin},
+    title      = {MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes},
+    journal    = {CoRR}, 
+    eprinttype = {arXiv},
+    url        = {https://arxiv.org/pdf/2412.19260}, 
+    year       = {2024}
+    }
+```

environments/medredqa/README.md

@@ -99,4 +99,34 @@ uv run vf-eval medredqa \
 ### Authors
 This environment has been put together by:
 
-Kunal Bagga - ([@bagga005](https://github.com/bagga005))
+Kunal Bagga - ([@bagga005](https://github.com/bagga005))
+
+
+
+### References
+
+```bibtex
+@inproceedings{nguyen-etal-2023-medredqa,
+    title = "{M}ed{R}ed{QA} for Medical Consumer Question Answering: Dataset, Tasks, and Neural Baselines",
+    author = "Nguyen, Vincent  and
+      Karimi, Sarvnaz  and
+      Rybinski, Maciej  and
+      Xing, Zhenchang",
+    editor = "Park, Jong C.  and
+      Arase, Yuki  and
+      Hu, Baotian  and
+      Lu, Wei  and
+      Wijaya, Derry  and
+      Purwarianti, Ayu  and
+      Krisnadhi, Adila Alfa",
+    booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = nov,
+    year = "2023",
+    address = "Nusa Dua, Bali",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.ijcnlp-main.42/",
+    doi = "10.18653/v1/2023.ijcnlp-main.42",
+    pages = "629--648"
+}
+
+```

environments/medxpertqa/README.md

@@ -48,3 +48,14 @@ Summarize key metrics your rubric emits and how they’re interpreted.
 | `reward` | Main scalar reward (weighted sum of criteria) |
 | `accuracy` | Exact match on target answer |
 
+
+### References
+
+```bibtex
+@article{zuo2025medxpertqa,
+  title={MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding},
+  author={Zuo, Yuxin and Qu, Shang and Li, Yifei and Chen, Zhangren and Zhu, Xuekai and Hua, Ermo and Zhang, Kaiyan and Ding, Ning and Zhou, Bowen},
+  journal={arXiv preprint arXiv:2501.18362},
+  year={2025}
+}
+```

environments/metamedqa/README.md

@@ -29,3 +29,23 @@ The evaluation environment is defined in `metamedqa.py` and uses the HuggingFace
 This environment has been put together by:
 
 Aymane Ouraq - ([@aymaneo](https://github.com/aymaneo))
+
+
+### References
+
+```bibtex
+@article{griot_large_2025,
+    title = {Large {Language} {Models} lack essential metacognition for reliable medical reasoning},
+    volume = {16},
+    issn = {2041-1723},
+    url = {https://doi.org/10.1038/s41467-024-55628-6},
+    doi = {10.1038/s41467-024-55628-6},
+    number = {1},
+    journal = {Nature Communications},
+    author = {Griot, Maxime and Hemptinne, Coralie and Vanderdonckt, Jean and Yuksel, Demet},
+    month = jan,
+    year = {2025},
+    pages = {642},
+}
+
+```

environments/mmlu_pro_health/README.md

@@ -59,3 +59,13 @@ Notes:
 | `accuracy` | (weight 1.0): 1.0 if parsed letter is correct, else 0.0 |
 
 
+### References
+
+```bibtex
+@article{wang2024mmlu,
+  title={Mmlu-pro: A more robust and challenging multi-task language understanding benchmark},
+  author={Wang, Yubo and Ma, Xueguang and Zhang, Ge and Ni, Yuansheng and Chandra, Abhranil and Guo, Shiguang and Ren, Weiming and Arulraj, Aaran and He, Xuan and Jiang, Ziyan and others},
+  journal={arXiv preprint arXiv:2406.01574},
+  year={2024}
+}
+```