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Shanghai Jiao Tong University
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Allocating rollout budgets based on state informativeness allows LLM agents to achieve superior performance in complex decision-making tasks without increasing computational costs.
DR-DCI achieves a remarkable 73.3% accuracy in agentic search tasks while efficiently scaling from 100K to 10M documents, outperforming traditional methods.
Masking stale observations can boost search agent accuracy, but only under specific conditions鈥攖oo much masking can backfire dramatically.
A clever two-stage agent using smaller models can produce better, more substantive peer reviews than brute-force application of the largest LLMs.
Today's best AI agents still fail more than half the time on real-world tasks combining vision, search, and coding, revealing critical gaps in reasoning and tool use.
