In-context learning (ICL) has achieved strong results across a wide range of large language model (LLM) tasks. However, its effectiveness for problems that require step-by-step deduction, such as mathematical reasoning, is still not well understood. One major limitation of existing ICL methods is that demonstrations ar...
Ang Gao, Chang-Shuo Zhang, Xiao Zhang et al.· Proceedings of the 35th ACM...· 0 citations
Recommender systems often need to balance multiple conflicting objectives. Controllable Multi-Objective Recommendation (CMOR) enables dynamic adjustment of objective trade-offs at inference time without retraining, adapting to shifting user or system requirements. Existing CMOR approaches typically rely on output-level...
Zi-Dong Jia, Guo-Fu Xie, Xiao Zhang· Proceedings of the 35th ACM...· 0 citations
DLLM-Searcher is proposed, an optimization framework for dLLM-based Search Agents that designs a two-stage post-training pipeline encompassing Agentic Supervised Fine-Tuning and Agentic Variance-Reduced Preference Optimization (Agentic VRPO), which enhances the backbone dLLM's information seeking and reasoning capabili...
Jiahao Zhao, Shaoxuan Xu, ZhongXiang Sun et al.· Annual International ACM SIG...· 0 citations
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