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…
探讨大语言模型如何跨越自我知识局限，在推理与检索中传播不确定性。
arXiv:2607.25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate r…
Hawkes过程结合LLM，让智能体文本模拟中的语义不确定性传播更精准可控。
arXiv:2605.23043v1 Announce Type: new Abstract: Agentic text-simulation systems write in sequence, with each item becoming possible context for later …
论文揭示AIVAT方差缩减技术中的启发式病态，并提出不确定性传播进一步降低方差，提升多智能体性能评估精度。
arXiv:2605.14261v1 Announce Type: new Abstract: How should an agent's performance in a multiagent environment be evaluated when there is a limited sam…