摆脱低效回退，用信息增益精准剪枝树形搜索路径，多轮LLM智能体告别闷头试探
arXiv:2607.06223v1 Announce Type: new Abstract: Reinforcement learning has become a promising paradigm for improving large language model (LLM) agents…
结合患者类比与信息增益，让LLM临床推理更精准、更可解释，方法新颖。
arXiv:2604.06684v2 Announce Type: replace Abstract: Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in …
新方法InfoMem用答案条件信息增益训练长上下文记忆代理，大幅提升模型知识检索与记忆能力。
arXiv:2606.03329v1 Announce Type: new Abstract: Long-context tasks require LLMs to identify and preserve answer-relevant information from large contex…
让LLM Agent学会主动追问澄清：用信息增益量化不确定性，提升任务成功率与交互效率
arXiv:2606.03135v1 Announce Type: new Abstract: Large Language Model (LLM) agents often operate under underspecified user instructions, where latent u…
用条件信息增益(CIG)驱动智能体探索，为强化学习探索策略提供新思路。
arXiv:2605.20878v1 Announce Type: new Abstract: Intrinsic rewards for exploration in reinforcement learning condition on different contexts: lifelong …
揭示自我对弈仅在自合成数据提供可学习信息增益时才有效演化，为AI训练策略提供关键理论指导。
arXiv:2603.02218v2 Announce Type: replace Abstract: Large language models (LLMs) make it plausible to build systems that improve through self-evolving…