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EASy: Towards Efficient LLM-Based Agentic System
EASy为LLM智能体系统提速增效，直击推理与行动协同的优化痛点
arXiv:2608.04588v1 Announce Type: cross Abstract: Agentic systems have emerged as a promising paradigm for solving complex tasks by coordinating speci…
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Uncertainty-Aware LLM-Guided Policy Shaping for Sparse-Reward Reinforcement Learning
让大模型在稀疏奖励环境中引导强化学习策略，通过不确定性估计提升决策可靠性，有代码可复现。
arXiv:2606.06673v1 Announce Type: new Abstract: Sparse rewards and heterogeneous task sequences remain persistent challenges in Reinforcement Learning…
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Self-evolving LLM agents with in-distribution Optimization
自进化LLM智能体通过分布内优化实现持续自我提升，无需人工干预
arXiv:2606.07367v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently emerged as powerful controllers for interactive agents in c…
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On Effectiveness and Efficiency of Agentic Tool-calling and RL Training
探索智能体工具调用与强化学习训练的效率与效果，为AI应用落地提供关键洞见。
arXiv:2606.00135v1 Announce Type: new Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with s…