Compositional Reasoning Depth Predicts Clinical AI Failure: Empirical Evidence Consistent with Transformer Compositionality Limits in Electronic Health Record Question Answering
arXiv:2606.16890v1 Announce Type: new Abstract: Aggregate accuracy benchmarks conceal a systematic structure in how large language models fail at electronic health record (EHR) question answering: questions requiring more inferential steps produce disproportionately more errors. Motivated by theoretical results on transformer compositionality limits, we introduce a pre-specified hop-count taxonomy -- the number of distinct reasoning steps required to answer a clinical question from an EHR -- as a principled predictor of model failure. We annotate 313 clinician-generated MedAlign EHR question-a
The proliferation of AI models in sensitive applications like healthcare is exposing their limitations, particularly in complex reasoning tasks, leading to focused research on understanding model failures.
This research provides a framework for predicting and understanding AI failures in critical domains, highlighting the need for more robust and compositionally aware AI systems, especially for white-collar automation.
The understanding of AI's 'known unknowns' in complex reasoning deepens, shifting the focus from aggregate accuracy to granular analysis of inferential steps and their implications for real-world deployment.
- · AI safety researchers
- · Developers of transparent AI
- · Healthcare providers with critical AI evaluation processes
- · Developers of black-box AI
- · Companies overselling AI capabilities without caution
- · Early adopters of unvalidated complex AI systems
Increased scrutiny and demand for explainability and compositional robustness in AI systems, particularly in highly regulated sectors.
A shift in AI development methodologies towards incorporating explicit reasoning steps and error-prediction mechanisms to mitigate 'compositionality limits'.
Potential for new regulatory frameworks for AI deployment that mandate testing for compositional reasoning depth and transparency of inferential pathways.
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Read at arXiv cs.CL