mlsanityData to 14 Oct 2026

CARE-RAG - Clinical Assessment and Reasoning in RAG

arXiv 2511.15994 ↗First posted 20 Nov 2025cs.AI cs.CLcited by 0

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Access to the right evidence does not guarantee that large language models (LLMs) will reason with it correctly. This gap between retrieval and reasoning is especially concerning in clinical settings, where outputs must align with structured protocols. We study this gap using Written Exposure Therapy (WET) guidelines as a testbed. In evaluating model responses to curated clinician-vetted questions, we find that errors persist even when authoritative passages are provided. To address this, we propose an evaluation framework that measures accuracy, consistency, and fidelity of reasoning. Our results highlight both the potential and the risks: retrieval-augmented generation (RAG) can constrain outputs, but safe deployment requires assessing reasoning as rigorously as retrieval.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 8 of its 24 references that are in the map. First posted on arXiv 20 Nov 2025.

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8 of 24 references.

References · 8

  1. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  3. 2025GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learningcited by 542
  4. 2025Agentic Context Engineering: Evolving Contexts for Self-Improving Language Modelscited by 398
  5. 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 315
  6. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 281
  7. 2025Retrieval-Augmented Generation with Conflicting Evidencecited by 63
  8. 2025Synergizing RAG and Reasoning: A Systematic Reviewcited by 27

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  1. 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights
  2. 2025Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
  3. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  4. 2025MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering
  5. 2025Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines
  6. 2026When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG
  7. 2025M-Eval: A Heterogeneity-Based Framework for Multi-evidence Validation in Medical RAG Systems
  8. 2025Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA