1 citations · 1 across the 10 of their papers we have counts for
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SeOPD: Self-Evolving LLMs via Online Policy Distillation from Self-Generated Chain-of-Thought
Xiaoshu Chen, Xiangyu Wong, Sihang Zhou +2
Recent advances in online policy self-distillation (OPSD) have demonstrated that large language models (LLMs) can improve their capabilities by leveraging external privileged infor…
AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory
Zijie Cao, Xijun Qu, Zhicheng Gu +7
Long-term memory is essential for large language model (LLM) agents to maintain consistency and personalization over extended interactions. Existing memory systems typically rely o…
Harness-G: A Graph-Structured Harness for Search Agents
Yanning Hou, Haoyuan Chen, Sihang Zhou +7
Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. C…
UXBench: Benchmarking User Experience in AI Assistants
Mengze Hong, Xia Zeng, Zeyang Lei +27
As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present \textbf{UXBench}, the…
Decoupled Travel Planning with Behavior Forest
Duanyang Yuan, Sihang Zhou, Yanning Hou +7
Behavior sequences, composed of executable steps, serve as the operational foundation for multi-constraint planning problems such as travel planning. In such tasks, each planning s…
AtomicRAG: Atom-Entity Graphs for Retrieval-Augmented Generation
Yanning Hou, Duanyang Yuan, Sihang Zhou +5
Recent GraphRAG methods integrate graph structures into text indexing and retrieval, using knowledge graph triples to connect text chunks, thereby improving retrieval coverage and…
ImgCoT: Compressing Long Chain of Thought into Compact Visual Tokens for Efficient Reasoning of Large Language Model
Xiaoshu Chen, Sihang Zhou, Ke Liang +2
Compressing long chains of thought (CoT) into compact latent tokens is crucial for efficient reasoning with large language models (LLMs). Recent studies employ autoencoders to achi…