大模型对话总是冗长？Schema级延迟通道带来更简洁的LLM交互新思路。
Article URL: https://raw.githubusercontent.com/noamt/kir/cfdc97de467de8d84d60f073773c0dcfd9975849/paper/arxiv/main.pdf Comments URL: https://news.ycom…
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…
研究自然语言反馈是否真比单纯重复尝试更有效，揭示多轮语言智能体性能提升的真实驱动力。
arXiv:2606.30774v1 Announce Type: new Abstract: We study when natural-language feedback produces improvement beyond the gains obtainable from repeated…
让AI智能体学会“适时退出”，用选择性放弃大幅提升多轮工具调用场景的安全性。
arXiv:2510.16492v4 Announce Type: replace Abstract: As Large Language Model (LLM) agents increasingly operate in complex environments with real-world …
该工具基于多轮对话理解用户意图，实现高效图像检索，创新融合记忆增强机制
arXiv:2605.17365v1 Announce Type: new Abstract: Different from traditional text-to-image retrieval tasks, chat-based image retrieval allows the human-…