aiqbench analysis
MedEvoEval introduces a novel longitudinal benchmark for assessing how AI doctor agents acquire evidence within clinical episodes and retain experience across them, moving beyond static QA metrics to evaluate memory maturation and resource allocation. This framework provides critical infrastructure for understanding the evolution of agentic systems in high-stakes domains where process costs and cross-episode learning are central to performance.
MedEvoEval: Evaluating Continual Evolution of Doctor Agents through Simulated Clinical Episodes
arXiv:2606.28900v2 Announce Type: replace Abstract: Doctor agents are moving beyond single-turn answer generation toward evolving clinical decision systems that acquire evidence within episodes and reuse experience across episodes. Fixed-input medical QA and many interactive benchmarks provide limited support for evaluating how episode-level decisions interact with cross-episode experience. We introduce MedEvoEval, an executable longitudinal evaluation framework based on action-gated simulated outpatient episodes. Each case is converted into role-specific patient, examination, and manager views; evidence is revealed through valid actions, and structured traces link observations, actions, final outputs, scores, and optional experience write-back. Experiments show that these traces expose process costs hidden by final-answer scoring, characterize how MDT-style consultation reallocates resources, and support analyses of memory maturation, held-out transfer, update-stage response, and backward retention. On 200 held-out cases, the mature-memory condition with sequential write-back reduces interaction turns by 0.915 and improves composite utility by 0.0483 after Holm correction. MedEvoEval provides a concrete basis for evaluating experience-driven improvement, transfer, and retention. The accompanying artifact provides 700 processed episode records with provenance, role-specific schemas, and execution, scoring, and analysis code.
Abstract:Doctor agents are moving beyond single-turn answer generation toward evolving clinical decision systems that acquire evidence within episodes and reuse experience across episodes. Fixed-input medical QA and many interactive benchmarks provide limited support for evaluating how episode-level decisions interact with cross-episode experience. We introduce MedEvoEval, an executable longitudinal evaluation framework based on action-gated simulated outpatient episodes. Each case is converted into role-specific patient, examination, and manager views; evidence is revealed through valid actions, and structured traces link observations, actions, final outputs, scores, and optional experience write-back. Experiments show that these traces expose process costs hidden by final-answer scoring, characterize how MDT-style consultation reallocates resources, and support analyses of memory maturation, held-out transfer, update-stage response, and backward retention. On 200 held-out cases, the mature-memory condition with sequential write-back reduces interaction turns by 0.915 and improves composite utility by 0.0483 after Holm correction. MedEvoEval provides a concrete basis for evaluating experience-driven improvement, transfer, and retention. The accompanying artifact provides 700 processed episode records with provenance, role-specific schemas, and execution, scoring, and analysis code.
Camera-ready version accepted at NeurIPS 2026, Evaluations and Datasets Track. Code and data are available at this https URL
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2606.28900 [cs.AI]
(or arXiv:2606.28900v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.28900 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 286 words
