Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
Deep research agents increasingly automate complex information-seeking tasks, producing evidence-grounded reports via multi-step reasoning, tool use, and synthesis. Their growing role demands scalable, reliable evaluation, positioning LLM-as-judge as a supervision paradigm for assessing factual accuracy, evidence use, and reasoning quality. Yet the reliability of these judges for deep research agents remains poorly understood, posing a critical meta-evaluation problem: before deploying LLM judges to supervise research agents, we must first evaluate the judges themselves. Existing meta-evaluations fall short in two ways: (1) reliance on coarse, subjective human-preference agreement; (2) fo...
| Authors | Leyao Wang, Yanan He, Peng Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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