Papers by Yaocheng Zhang

2 papers
CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic (2026.findings-acl)

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Challenge: Existing search agent pipelines rely on sparse outcome rewards, leading to inefficient exploration and unstable training.
Approach: They propose a tool-integrated reasoning framework that provides turn-level feedback via a retrospective critic mechanism.
Outcome: The proposed framework outperforms baselines in multi-hop reasoning benchmarks and achieves faster convergence and training stability.
AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning (2026.findings-acl)

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Challenge: Prior work limits search depth to reduce cost, but this often leads to underexploration of complex questions.
Approach: They propose a reinforcement learning framework that evaluates each search step via self-generated intermediate answers.
Outcome: Extensive experiments on multiple benchmarks show that AutoSearch achieves a superior accuracy-efficiency trade-off, alleviating over-searching while preserving search quality.

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