Papers by Yijie Hao
Reasoning Traces Shape Outputs but Models Won’t Say So (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) generate explicit reasoning traces before producing answers, offering a window into their decisionmaking. |
| Approach: | They propose a method that injects synthetic reasoning snippets into a model’s reasoning trace and measures whether the model follows the injected reasoning and acknowledges doing so. |
| Outcome: | The proposed method reveals that models refuse to disclose their influence when asked to explain their changed answers. |
Beyond Facts: Evaluating Intent Hallucination in Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) produce unsatisfactory results when faced with complex queries containing multiple conditions. |
| Approach: | They propose a benchmark for intent hallucination that covers 20,068 problems and an automatic LLM generation evaluation metric for detecting intent hallucinosis. |
| Outcome: | The proposed benchmark covers query-only and retrieval-augmented generation (RAG) setups with varying topics and difficulty. |
Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)
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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang
| Challenge: | Existing methods for training effective AI agents often resort to synthetic data generation. |
| Approach: | They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance . |
| Outcome: | The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset. |