REFLECT benchmark shows current LLM judges achieve below 55% accuracy detecting failures in evidence-based research agents, especially on evidence verification.
Yifei, Allen Chang, Chaitanya Malaviya, and Mark Yatskar
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
BRIGHT-Pro and RTriever-Synth advance reasoning-intensive retrieval by adding multi-aspect evidence evaluation and aspect-decomposed synthetic training, with the fine-tuned RTriever-4B showing gains over its base model.
Rubric-based RL verifiers can be gamed via partial criterion satisfaction and implicit-to-explicit tricks, yielding proxy gains that do not improve quality under rubric-free judges; stronger verifiers reduce but do not eliminate the mismatch.
citing papers explorer
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Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
REFLECT benchmark shows current LLM judges achieve below 55% accuracy detecting failures in evidence-based research agents, especially on evidence verification.
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Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems
BRIGHT-Pro and RTriever-Synth advance reasoning-intensive retrieval by adding multi-aspect evidence evaluation and aspect-decomposed synthetic training, with the fine-tuned RTriever-4B showing gains over its base model.
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Reward Hacking in Rubric-Based Reinforcement Learning
Rubric-based RL verifiers can be gamed via partial criterion satisfaction and implicit-to-explicit tricks, yielding proxy gains that do not improve quality under rubric-free judges; stronger verifiers reduce but do not eliminate the mismatch.