LLM代理能否智能地“不作为”？新研究探索Agent安全决策边界，为AI可靠性提供关键视角。
arXiv:2607.10059v1 Announce Type: new Abstract: Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, ye…
提出一种新颖的“有界弃权”成对排序学习方法，提升排序模型在不确定样本上的决策可靠性。
arXiv:2505.23437v2 Announce Type: replace-cross Abstract: Ranking systems influence decision-making in high-stakes domains like health, education, and…
LLM是否具备独立元认知？新研究揭示置信度路由与选择性弃权假设的潜在缺陷。
arXiv:2605.24299v1 Announce Type: new Abstract: Confidence-weighted routing, selective abstention, and ensemble weighting all assume that a model's st…
具身机器人何时该说“不”？这篇论文提出“Yes-Man综合征”并构建弃权基准测试，探索机器人安全拒绝指令的关键问题。
arXiv:2605.20544v1 Announce Type: cross Abstract: Vision-language models (VLMs) are used as high-level planners for embodied agents, translating natur…
提出自我感知RAG方法，解决检索知识与参数知识冲突时的信任与弃权决策问题。
arXiv:2605.18792v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves large language models (LLMs) by incorporating external…