As skill libraries grow, large language model agents must retrieve reusable skills from candidates that often share the same topic and vocabulary but implement different capabilities. Retrieval is limited not only by the scorer but also by the text being scored: a document may describe what a skill does without stating...
Zifei Wang, Wei Wen, Qian Ji et al.· 4 citations· ⚡1
Parallel speculative drafting generates multiple candidates in one backbone pass, but independent token selection can produce inconsistent continuations that shorten the accepted prefix. Existing methods mostly leave conditional decoding to a lightweight module after the backbone, which limits the flow of predecessor i...
Hao-Hui Zhang, Ke-Yu Chen, Hao-Cheng Sun et al.· 0 citations
LGM is presented, a novel neuro-symbolic framework that shifts long-term memory disentanglement into a continuous latent space and significantly outperforms state-of-the-art baselines in capturing both explicit and implicit preferences while enabling personalized responses.
Cai Ke, Xing-Hao Chen, Xiao-Yu Shen et al.· 1 citation
Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles, demonstrating that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.
Jiayi Kuang, Ying-Hui Li, Yun-Ze Song et al.· 0 citations
Reinforcement learning holds significant potential for training large language models to handle multi-turn interactive tasks, but directly training with outcome rewards often results in slow convergence due to the sparsity of signals and the lack of fine-grained feedback.
Qiang Liu, Taian Guo, Ruizhi Qiao et al.· 0 citations
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