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Author

Haibo Shi

Observed name: Shi, Haibo
Institution: Tencent (China)
Observed institution: Tencent (China)
OpenAlex ID: A5133562699
ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration
2026 · Chen, Guo, Li, Ziwen, et al. · arXiv (Cornell University)
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Training Documents Reranker with Search Rubrics for Deep Research Agent
2026 · Liu, Wenhan, Lu, Yu, et al. · arXiv (Cornell University)
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Search-G1: Co-Evolving Representation-Based Rewards for Selective Grounded Search
2026 · Cheng, Ruoxi, Ma, Haoxuan, et al. · arXiv (Cornell University)
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OPOD: On-Policy Omni Distillation
2026 · Zhao, Tong, Hu, Yuyang, et al. · arXiv (Cornell University)
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Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking
2026 · Jiang, Kailin, Liu, Lei, et al. · arXiv (Cornell University)
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Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
2026 · Lin, Zekai, Xue, Chao, et al. · arXiv (Cornell University)
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Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models
2026 · Xue, Chao, Wang, Yao, et al. · arXiv (Cornell University)
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Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty
2026 · Xue, Chao, Wang, Yao, et al. · arXiv (Cornell University)
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When Safety Becomes a Vulnerability: Exploiting LLM Alignment Homogeneity for Transferable Blocking in RAG
2026 · Li, Junchen, Xu, Liang, et al. · arXiv (Cornell University)
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Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models
2026 · Chao Xue, Yao Wang, et al. · Proceedings of the conference - Association for Computational Linguistics. Meeting
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Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty
2026 · Chao Xue, Yao Wang, et al. · Findings of the Association for Computational Linguistics: ACL (Findings of ACL)
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Beyond Polarization: The Generative Constraint of Chain-of-Thought in Pointwise Reranking
2026 · Xiaoyang Chen, Jie Liu, et al. · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)
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Works count: 12
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