让大模型从“被动应答”走向“主动服务”，教你训练会察言观色的个性化AI代理，附完整方法框架。
arXiv:2511.02208v2 Announce Type: replace Abstract: Despite rapid progress, current AI agents are primarily optimized for isolated task completion. We…
揭秘大模型智能体如何通过工具实现“遗忘”，为安全与能力平衡提供新思路。
arXiv:2608.21544v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as tool-augmented agents, where responses can…
LLM智能体长任务中记忆失效的痛点，MemGuard用验证信号让存储经验长期可靠。
arXiv:2608.21867v1 Announce Type: new Abstract: LLM agents are moving from single-prompt use to long task streams in which reusable memory becomes a c…
LLM代理落地关键在于一致性与边界感知，TRACE用自进化技能库破解可靠性难题，值得关注。
arXiv:2608.22793v1 Announce Type: cross Abstract: Reliable deployment of LLM agents in user-facing products depends not on raw task-solving ability bu…
用动态本体给智能体装上知识骨架，可靠性与效率双提升！
arXiv:2608.22974v1 Announce Type: new Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented a…
系统化诊断长时程安全LLM代理的失败根因，突破端到端指标的盲区。
arXiv:2608.20563v1 Announce Type: cross Abstract: Long-horizon security LLM agents must carry information and decisions across many dependent interact…
不再只关注序列预测，而是引入关系不确定性传播，让LLM智能体在结构化决策中更可靠，值得深入研读。
arXiv:2608.16002v1 Announce Type: cross Abstract: Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agent…
针对LLM智能体的提示词优化新方法，把约束条件纳入优化过程，让Agent更懂规则、少犯错。
arXiv:2608.16068v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use …
长时LLM智能体执行中断怎么办？这篇论文提出可恢复执行机制，为复杂Agent任务提效护航。
arXiv:2608.14380v1 Announce Type: new Abstract: Many real-world tasks require LLM agents to interact with their environments over long execution horiz…
当AI代理多到找不着，这家“工厂”专治选择困难，精准检索领域专属智能体。
arXiv:2608.09934v1 Announce Type: new Abstract: Large language model (LLM) agents improve task performance by decomposing problems into role-specializ…
当大模型遇上神经符号验证，自主系统可信度迎来新解法，一篇值得深读的前沿方法。
arXiv:2604.02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data. However, they do not prov…
给LLM Agent的技能执行加一道运行时安全锁，让智能体在真实场景中更可靠落地。
arXiv:2608.09253v1 Announce Type: new Abstract: LLM agents are increasingly equipped with skills to perform complex tasks through multi-step reasoning…
给大模型智能体装上“时间线图记忆”，用证据支撑时序推理，值得AI研究者一读
arXiv:2608.08055v1 Announce Type: new Abstract: Large language model (LLM) agents that assist users over weeks of conversation must remember what is c…
仅靠 bash 工具驱动的极简 LLM 代理，轻量设计思路让人眼前一亮。
Article URL: https://github.com/lgxz/dora Comments URL: https://news.ycombinator.com/item?id=49254791 Points: 1 # Comments: 0
Fisher-R1用强化学习训练AI智能体做统计假说检验，显著提升可靠性，值得关注的研究。
arXiv:2608.07437v1 Announce Type: new Abstract: Reliable hypothesis testing is the foundation of many empirical scientific claims. Large language mode…
别再甩锅给模型了，工具Schema设计才是Agent翻车的重灾区。
Originally published on Loop & Retry — field notes on building LLM agents that survive production. Most agent bugs I've chased weren't in the mode…
LLM智能体自我进化可能反噬，预承诺门控机制防止技能污染，揭秘新解法。
arXiv:2608.05810v1 Announce Type: new Abstract: Self-evolving agents accumulate capability by distilling reusable skills from their execution trajecto…
为LLM智能体打造风险感知世界模型的运行时护栏，用世界模型预判危险，安全高效两不误。
arXiv:2608.05695v1 Announce Type: new Abstract: As large language model (LLM) agents increasingly invoke external tools and interact with real-world s…
LLM智能体能否真正持续进化？首个持续技能学习基准框架，直击智能体能力跃迁核心难题
arXiv:2608.03874v1 Announce Type: cross Abstract: Modern agent frameworks equip large language models with external skill libraries to solve complex t…
LLM Agent故障监控的新思路：确定性验证校准，摆脱冷启动高误报。
arXiv:2608.02464v1 Announce Type: new Abstract: LLM agents fail mid-episode -- they loop, cascade tool errors, drift off goal, fabricate results, or s…