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*DSEBench: A Test Collection for Explainable Dataset Search with Examples. __Proc. APWeb__
*InteractScience: Programmatic and Visually-Grounded Evaluation of Interactive Scientific Demonstration Code Generation. __Proc. ICML__
*Improving code translation with syntax-guided and semantic-aware preference optimization. __Proc. IJCAI__
*BCaLLM: Call graph-guided Python breaking change detection with large language models. __Proc. ISSTA__
*Bootstrapping code translation with weighted multilanguage exploration. __Proc. ACL__
*How do answer tokens read reasoning traces? Self-reading patterns in thinking LLMs for quantitative reasoning. __Findings of ACL__
*To diff or not to diff? Structure-aware and adaptive output formats for efficient LLM-based code editing. __Findings of ACL__
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*CN-PDS: Facilitating Dataset Search with LLMs and Its Application to China's Public Data. __Data Sci. Eng. %%(color:red;)(CCKS 2025最佳应用论文奖)%%__
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*CR-TKGQA: A Temporal Knowledge Graph Question Answering Dataset Involving Complex Reasoning. __Proc. ESWC__
*CR-TKGQA: A Temporal Knowledge Graph Question Answering Dataset Involving Complex Reasoning. __Proc. ESWC %%(color:red;)(最佳资源论文提名)%%__
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*Practical Group Steiner Tree Algorithms for Web Applications with Many Groups. __Proc. WWW__
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*ProtSAE: Disentangling and interpreting protein language models via semantically-guided sparse autoencoders. __Proc. AAAI__
*Answering the unanswerable is to err knowingly: Analyzing and mitigating abstention failures in large reasoning models. __Proc. AAAI__
*Do we truly need so many samples? Multi-LLM repeated sampling efficiently scales test-time compute. __Proc. AAAI__
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!2026
*Qicheng Shan, Yuxuan Yang, Gong Cheng: Practical Group Steiner Tree Algorithms for Web Applications with Many Groups. __Proc. WWW__
*Xiangyu Liu, Haodi Lei, Yi Liu, Yang Liu, Wei Hu: ProtSAE: Disentangling and Interpreting Protein Language Models via Semantically-Guided Sparse Autoencoders. __Proc. AAAI__
*Jianhao Chen, Zishuo Xun, Bocheng Zhou, Han Qi, Hangfan Zhang, Qiaosheng Zhang, Yang Chen, Wei Hu, Yuzhong Qu, Shuyue Hu: Do We Truly Need So Many Samples? Multi-LLM Repeated Sampling Efficiently Scales Test-Time Compute. __Proc. AAAI__
*Yi Liu, Xiangyu Liu, Zequn Sun, Wei Hu: Answering the Unanswerable Is to Err Knowingly: Analyzing and Mitigating Abstention Failures in Large Reasoning Models. __Proc. AAAI__
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*CN-PDS: Facilitating Dataset Search with LLMs and Its Application to China's Public Data. __Data Sci. Eng. %%(color:red;)(CCKS 2025最佳应用论文奖)%%__
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