mlsanityData to 14 Oct 2026

Rethinking LLM Verification: Evidence Structure, Uncertainty, and Selective Refinement

arXiv 2608.10725 ↗First posted 11 Aug 2026cs.CV cs.SCcited by 0

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Large language models (LLMs) often rely on shortcuts rather than systematic reasoning, raising safety concerns in medical applications. Allowing models to abstain when uncertain improves reliability but introduces a coverage accuracy tradeoff. We propose a two-stage framework for medical hypothesis verification in multiple-choice settings that manages this tradeoff through targeted ontology grounding, applied only when the model abstains. We show that abstention is not random but reflects genuine uncertainty, with abstained predictions associated with lower confidence. Across two frontier models (GPT-5.5, accessed via the Azure OpenAI API, and DeepSeek-R1), the proposed framework improves question-level accuracy by 9.6 percentage points (82.9% to 92.5%) and hypothesis-level accuracy by 4.2 percentage points (92.0% to 96.2%). Our experiments conducted on MedReason and MedQA show that abstention can be repurposed as a control signal for selective reasoning refinement, achieving knowledge-graph-level performance without explicit knowledge graph construction.

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 20 references that are in the map. First posted on arXiv 11 Aug 2026.

Builds on

8 of 20 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards13 Jun
  2. 2025MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs1 Apr
  3. 2025Medical Reasoning in LLMs: An In-Depth Analysis of DeepSeek R127 Mar

Other references · 5

  1. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  2. 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 121
  3. 2025Medical Hallucinations in Foundation Models and Their Impact on Healthcarecited by 93
  4. 2026CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicinecited by 2
  5. 2026Enhancing reasoning accuracy in large language models during inference timecited by 1

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  1. 2025Automating Expert-Level Medical Reasoning Evaluation of Large Language Models
  2. 2025MedOmni-45deg: A Safety-Performance Benchmark for Reasoning-Oriented LLMs in Medicine
  3. 2026Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty
  4. 2025Stabilizing Reasoning in Medical LLMs with Continued Pretraining and Reasoning Preference Optimization
  5. 2026Medical Reasoning with Large Language Models: A Survey and MR-Bench
  6. 2025The Knowledge-Reasoning Dissociation: Fundamental Limitations of LLMs in Clinical Natural Language Inference
  7. 2026Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation
  8. 2025Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications