JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks

Abstract:Evaluating agentic AI on open-ended professional tasks faces a fundamental dilemma between rigor and flexibility. Static rubrics provide rigorous, reproducible assessment but fail to accommodate diverse valid response strategies, while LLM-as-a-judge approaches adapt to individual responses yet suffer from instability and bias. Human experts address this dilemma by combining domain-grounded principles with dynamic, claim-level assessment. Inspired by this process, we propose \textbf{JADE}, a two-layer evaluation framework. Layer 1 encodes expert knowledge as a predefined set of evaluation skills, providing stable evaluation criteria. Layer 2 performs report-specific, claim-level evaluation to flexibly assess diverse reasoning strategies, with evidence-dependency gating to invalidate conclusions built on refuted claims. Experiments on BizBench show that JADE improves evaluation stability and reveals critical agent failure modes missed by holistic LLM-based evaluators. We further demonstrate strong alignment with expert-authored rubrics and effective transfer to HealthBench and this http URL, covering medical and 10-domain professional evaluation settings. Code and data are available at this https URL.

Artificial Intelligence (cs.AI)

arXiv:2602.06486 [cs.AI]

(or arXiv:2602.06486v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2602.06486 Focus to learn more arXiv-issued DOI via DataCite

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