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

• URL: http://arxiv.org/abs/2602.06486v1
• Date: Fri, 06 Feb 2026 08:26:09 GMT
• Title: JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks
• Authors: Lanbo Lin, Jiayao Liu, Tianyuan Yang, Li Cai, Yuanwu Xu, Lei Wei, Sicong Xie, Guannan Zhang,
• Abstract summary: We propose JADE, a two-layer evaluation framework for agentic AI.<n> Layer 1 encodes expert knowledge as a predefined set of evaluation skills.<n> Layer 2 performs report-specific, claim-level evaluation to flexibly assess diverse reasoning strategies.
• Score: 14.14645345504797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 a medical-domain benchmark, validating JADE across professional domains. Our code is publicly available at https://github.com/smiling-world/JADE.

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