Effective skill identification is crucial for LLM agents to reuse task-solving experience for complex task execution, which however remains challenging as agents must accurately identify useful skills from a large skill gallery and invoke them on demand. Existing studies have explored various strategies for skill retri...
Shuo Liu, Yu-Tong Yang, Haonan Xiao et al.· 2 citations
RG-ICL is introduced, a training-free inference framework that retrieves query-aligned demonstrations using frozen encoders, without task-specific parameter updates, and indicates that for medical image interpretation, curating which reference cases an MLLM sees is a practical alternative to retraining it.
Min-Da Zhao, Fang-Yu Hu, Yan Luo et al.· 0 citations
ActTraitBench is proposed, a human-grounded evaluation framework for measuring personality consistency in LLMs that establishes one-to-one mappings between psychometric facets and behavioral paradigms and applies Distributional Calibration via Quantile Mapping to reduce distributional mismatch between LLM-judge scores...
Yu-Tong Yang, Chenxi Miao, Wei-Kang Li et al.· arXiv.org· 2 citations
This work proposes SkillDreamer, a novel framework that first infers the capabilities necessary for task execution, then imagines how to realize these capabilities by generating pseudo skills, and finally leverages such prospective information to bridge the gap between objective-oriented task queries and execution-orie...
Shuo Liu, Yu-Tong Yang, Haonan Xiao et al.· 3 citations
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