Large language model (LLM) agents tackle complex tasks through reasoning and interaction with external environments. Agent skills provide persistent, transferable capabilities that guide task planning, inform contextual decisions, support tool use, and enable executable procedures. Yet existing skill-evolution methods...
Ye-Wei Zhao, De-Rong Xu, Wei Chen et al.· 0 citations
Layer-level pruning is attractive for large language model (LLM) deployment as it reduces the model size while preserving a dense architecture that can be efficiently executed on standard hardware. Current methods typically follow a one-shot paradigm, which first removes selected layers to reach a target model size and...
Liang-Sheng Xu, Shu-Fan Shen, Zhong-Ni Hou et al.· 0 citations
The proposed ToolEX (Tool Equivalent eXpansion), a framework that automatically discovers and annotates the tool combinations functionally equivalent to the labeled ones, and confirms that the one-to-one problem extends beyond tool retrieval.
Yan-Yu Zhu, Chen-Heng Zhang, Shao-Shen Chen et al.· 0 citations
This work introduces SAKI (Supervision Allocation with KL-constrained Interpolation), which combines a KL-constrained teacher-guided rollout with maximal coupling and reuses realized accept/correction events to route token-level supervision.
Miteto Wei, Xiao-Han Wang, Ze-Hao Chen et al.· 0 citations
Temperature-Grouped Reinforcement Learning (TGRL), which turns temperature-induced diversity into an explicit training signal, and reaches equivalent accuracy up to 36% faster than strong RLVR baselines without expanding the rollout budget.
Zi-Han Lin, Xiao-Han Wang, Jie Cao et al.· 0 citations
Experiments in controlled multi-solution environments and continual post-training settings across multiple model scales demonstrate that CoKL achieves a more favorable balance between target-task improvement and prior-capability retention than existing regularization methods.
Li Wang, Xiao-Dong Lu, Xiao-Han Wang et al.· 0 citations
OSOL, which designates a focus domain at each iteration, uses the preceding checkpoint footprint to rank token-level rebound risk, and applies a drift-ranked, adaptively scaled correction within the standard GRPO update, is proposed, which suppresses the targeted cross-step output backtracking component.
Zi-Han Lin, Xiao-Han Wang, Jie Cao et al.· 1 citation
Temporal Semantic Memory is proposed, a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory and incorporates the query's temporal intent on the semantic timeline.
Miao Su, Yu-Can Guo, Zhong-Ni Hou et al.· Annual Meeting of the Associ...· 12 citations
This work proposes ATLAS, a dual-horizon diagnostic evaluation framework for industrial tool-use agents that instantiates LLM judge interfaces as executable signals with explicit evidence scopes and decision boundaries, and evaluates ATLAS on Meituan Xiaotuan production traffic.
Wei Chen, Pei-Lun Zhou, Zhao-Yu Hu et al.· 0 citations
A new task, Behavior-Aware Travel Planning, which infers user preferences directly from past behaviors and generates personalized travel plans and proposes B2T-Agent, a reinforcement learning-based agent that leverages user behavior trajectories, interacts with external tools for preference-aligned retrieval, and maint...
Zihao Cheng, Yingyu Shan, Hongru Wang et al.· 1 citation
HiDiffTIR is proposed, a Hierarchical Difficulty-aware policy optimization framework for multi-turn TIR that consistently improves multi-turn TIR performance and tool invocation accuracy over strong RL baselines, highlighting the necessity of difficulty-aware credit assignment for effective policy optimization in tool-...
Yu-Can Guo, Xiao-Han Wang, Miao Su et al.· 0 citations
BASM is proposed, which augments each skill with explicit boundary fields, which transforms each retrieved skill from an unconditional action template into state-conditioned guidance: the agent applies the skill when its conditions hold, suppresses inapplicable tool calls when they do not, and issues targeted repairs w...
Zi-Han Lin, Zhen-Yu Chen, Jiawen Wei et al.· 1 citation
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