多智能体协作中错误会级联放大，这项研究用传播感知量化不确定性，给LLM集群装上风险仪表盘。
arXiv:2608.22130v1 Announce Type: cross Abstract: LLM-based multi-agent systems (MAS) solve complex tasks through communication among role-specialized…
集合LoRA适配器构建置信区间，让大模型区分“不知道”与“不确定”，告别过度自信的胡说八道。
arXiv:2608.23244v1 Announce Type: cross Abstract: Large language models (LLMs) often produce fluent but incorrect answers with unwarranted confidence.…
黑盒LLM分类推理的不确定性估计，巧用层级感知监督提升准确性。
arXiv:2608.22839v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for scientific decision support, yet reliable con…
黑盒大模型置信度不准？这项研究提出新方法，让答案可信度更可靠。
arXiv:2608.19323v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs). …
快速估计文本生成不确定性动态的新方法，让模型自我评估更高效精准。
arXiv:2608.19611v1 Announce Type: cross Abstract: LLM reasoning is stochastic, and so understanding a model requires grappling with the distribution o…
给LLM判卷装上“安全气囊”：不确定时检索或弃权，还带可证明风险保证。
arXiv:2608.17994v1 Announce Type: new Abstract: Using LLMs as judges has become standard practice for evaluating model outputs at scale. This is parti…
多模态大模型如何量化不确定性？这项研究为高风险场景下的可靠决策提供新思路。
arXiv:2608.17084v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) increasingly answer questions whose correctness depends on vi…
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…
大模型能否主动追问缺失信息？这项研究评估了多轮对话中的信息寻求能力，为AI“懂不懂装懂”给出答案。
arXiv:2608.14808v1 Announce Type: new Abstract: When a user question is underspecified, a capable model should recognize that its context is insuffici…
从语义不确定性切入，为层级多智能体协作提供全新编排思路，适合关注大模型Agent与系统优化的读者。
arXiv:2608.14707v1 Announce Type: new Abstract: As large language model (LLM)-based multi-agent systems become increasingly capable, coordinating agen…
系统评估五种LLM提示交付方式，用辅助不确定性信号提升系统综述筛查的可靠性与效率
arXiv:2608.14551v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for title-abstract screening in systematic review…
用消融实验揭开LLM自我反思的机制，看不确定性路由如何影响冲突预测准确率。
arXiv:2608.12322v1 Announce Type: cross Abstract: Self-reflection is widely assumed to improve LLM reasoning, yet which component drives the gain rema…
LLM不确定性新思路：无需额外训练，仅靠注意力路径的脆弱程度就能暴露模型“心虚”时刻，极具实用潜力。
arXiv:2608.11138v1 Announce Type: new Abstract: We propose that a model's uncertainty about a token is reflected not only in the breadth of its output…
跳出传统校验框架，用证据结构和不确定性指导选择性修正，给LLM验证提供了新思路。
arXiv:2608.10725v1 Announce Type: new Abstract: Large language models (LLMs) often rely on shortcuts rather than systematic reasoning, raising safety …
当临床信息不完整时，大模型可能“迷之自信”，这项研究揭示风险并敲响医疗AI安全警钟。
arXiv:2608.09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clin…
把个性化奖励拆解成可解释概率偏好，不确定性建模让LLM对齐更稳更准。
arXiv:2604.00997v2 Announce Type: replace Abstract: Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared …
针对RAG幻觉难题，提出基于归纳感知熵门控的不确定性估计新方法，直击熵基方法失效痛点。
arXiv:2603.21607v2 Announce Type: replace Abstract: While retrieval-augmented generation (RAG) significantly improves the factual reliability of LLMs,…
用信息度量拆解大模型不确定性，LogitScope为LLM可解释性提供全新分析框架
arXiv:2603.24929v2 Announce Type: replace-cross Abstract: Understanding and quantifying uncertainty in large language model (LLM) outputs is critical …
用追溯条件优化答案一致性，为LLM不确定性量化提供高效新方案，值得关注。
arXiv:2608.00422v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect …
大模型空间推理常“答非所证”，这项研究用符号不确定性量化给结论可靠性加把锁，值得关注。
arXiv:2608.00417v1 Announce Type: new Abstract: Although large language models (LLMs) can produce fluent spatial reasoning traces, their intermediate …