AutoResearchBench is a new benchmark showing top AI agents achieve under 10% success on complex scientific literature discovery tasks that demand deep comprehension and open-ended search.
Infoflow: Reinforcing search agent via reward density optimization
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Retrievers trained on agent trajectories via the LRAT framework improve evidence recall, task success, and efficiency in agentic search benchmarks.
CAPF improves Qwen3-4B exact-match scores from 44.7% to 48.5% on seven QA benchmarks by allowing privileged verifier feedback during RLVR training with attenuated credit for the feedback step.
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AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery
AutoResearchBench is a new benchmark showing top AI agents achieve under 10% success on complex scientific literature discovery tasks that demand deep comprehension and open-ended search.
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Learning to Retrieve from Agent Trajectories
Retrievers trained on agent trajectories via the LRAT framework improve evidence recall, task success, and efficiency in agentic search benchmarks.
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CAPF: Guiding Search-Agent Rollouts with Credit-Attenuated Privileged Feedback
CAPF improves Qwen3-4B exact-match scores from 44.7% to 48.5% on seven QA benchmarks by allowing privileged verifier feedback during RLVR training with attenuated credit for the feedback step.