mlsanityData to 14 Oct 2026

Advances in LLM Reasoning Enable Flexibility in Clinical Problem-Solving

arXiv 2601.11866 ↗First posted 17 Jan 2026cs.CLcited by 0

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Large Language Models (LLMs) have achieved high accuracy on medical question-answer (QA) benchmarks, yet their capacity for flexible clinical reasoning has been debated. Here, we asked whether advances in reasoning LLMs improve their cognitive flexibility in clinical reasoning. We assessed reasoning models from the OpenAI, Grok, Gemini, Claude, and DeepSeek families on the medicine abstraction and reasoning corpus (mARC), an adversarial medical QA benchmark which utilizes the Einstellung effect to induce inflexible overreliance on learned heuristic patterns in contexts where they become suboptimal. We found that strong reasoning models avoided Einstellung-based traps more often than weaker reasoning models, achieving human-level performance on mARC. On questions most commonly missed by physicians, the top 5 performing models answered 55% to 70% correctly with high confidence, indicating that these models may be less susceptible than humans to Einstellung effects. Our results indicate that strong reasoning models demonstrate improved flexibility in medical reasoning, achieving performance on par with humans on mARC.

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 31 of its 53 references that are in the map. First posted on arXiv 17 Jan 2026.

Builds on

31 of 53 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoning5 Feb
  2. 2023Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales12 Dec
  3. 2022Can large language models reason about medical questions?17 Jul

Other references · 28

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  5. 2023Mistral 7Bcited by 3,164
  6. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  7. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
  8. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  9. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  10. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 382
  11. 2023BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Taskscited by 201
  12. 2023Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solvecited by 178
16 more
  1. 2023A Study of Generative Large Language Model for Medical Research and Healthcarecited by 163
  2. 2024Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Modelscited by 122
  3. 2024Cognitive Bias in Decision-Making with LLMscited by 106
  4. 2024ARC Prize 2024: Technical Reportcited by 104
  5. 2024Chain of Thoughtlessness? An Analysis of CoT in Planningcited by 103
  6. 2023The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domaincited by 101
  7. 2025Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2cited by 97
  8. 2025DeepSeek-R1 Thoughtology: Let's think about LLM Reasoningcited by 92
  9. 2022Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolutioncited by 78
  10. 2024Calibrating Large Language Models with Sample Consistencycited by 73
  11. 2023Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Taskscited by 55
  12. 2024Exploring Prosocial Irrationality for LLM Agents: A Social Cognition Viewcited by 38
  13. 2025Position: Don't Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapointscited by 23
  14. 2024When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Modelscited by 22
  15. 2023Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Datasetcited by 17
  16. 2025ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoningcited by 17

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