Benchmark Integrity and Reasoning-Trace Errors in Medical Question Answering With Large Language Models: Mixed Methods Study With Sparse Autoencoders

Background: Large language models (LLMs) show promise for enhancing diagnostic accuracy and clinical decision-making. However, prevailing evaluations rely on examination-based benchmarks such as MedQA. Furthermore, the internal mechanisms driving both correct and incorrect reasoning in LLMs remain poorly understood, limiting opportunities for targeted improvement. Objective: This study aimed to investigate failure modes of reasoning-based LLMs in medicine by (1) auditing the integrity of the MedQA benchmark, (2) developing a clinically informed taxonomy of reasoning errors across multiple major LLMs, and (3) testing a mechanistic intervention using sparse autoencoders (SAEs) to modulate reasoning characteristics and improve accuracy in medical question answering benchmarks. Methods: We evaluated OpenAI o1 on the MedQA and cross-referenced incorrect answers against original source platforms to identify benchmark flaws including missing figures and postrelease ambiguity corrections. For the 37 confirmed model failures remaining after exclusion of flawed items, we developed a reasoning error taxonomy through iterative inductive coding by 2 independent reviewers (JL and SL) and validated it on three major LLMs (ie, OpenAI GPT-4.5, OpenAI o3-mini, and DeepSeek-R1). We then trained an SAE on the DeepSeek-R1-Distill-Llama-8B model using MedQA-derived reasoning traces. Reasoning-specific features were identified using ReasonScore and subjected to activation steering at 2 strengths. Model accuracy, reasoning trace length, and hallucination metrics were measured across MedQA, MedMCQA, and PubMedQA. Hallucinations were evaluated using an LLM-as-a-judge (OpenAI GPT-5-mini) and validated on a stratified manual sample of 100 claims. Results: Forty-one percent of initial OpenAI o1 errors reflected benchmark problems, including missing figures (22%) and ambiguities subsequently corrected on the source platforms (19%). Neither OpenAI o1 nor OpenAI o3-mini explicitly flagged these flawed items, while GPT-5.2 identified a small subset, suggesting that question-integrity recognition remains limited and model-dependent. Among the 37 confirmed errors, our taxonomy classified failures into four categories: Information Synthesis Errors, Therapeutic Decision Errors, Diagnostic Reasoning Errors, and Foundational Principle Errors. Activation steering of reasoning-specific SAE features improved accuracy on MedQA and PubMedQA, with a consistent positive trend on MedMCQA. The greatest gains were observed at steering strength 2 (MedQA: 0.568-0.597 and PubMedQA: 0.708-0.739). Steering also increased reasoning-trace length substantially, with no significant correlation between verbosity and accuracy. Five functional feature categories were identified, with alignments to the error taxonomy. Conclusions: These findings reveal two distinct sources of unreliability in medical LLM evaluation: benchmark-level integrity gaps that misattribute model failure and recurrent model-level reasoning patterns potentially amenable to mechanistic correction. Notably, the benchmark issues identified here do not reflect static flaws in the original source platforms, which have since corrected many problematic items, but rather a failure to propagate those corrections to derived benchmarks. The alignment between SAE-identified feature categories and the error taxonomy further suggests that reasoning failures reflect structured internal processes that can be targeted at the feature level.