从智能体行为反推时间概念认知，保形可解释性让LLM黑箱决策路径有据可循，机制解读新突破。
arXiv:2604.19775v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, …
压缩LLM时，准确率不是唯一指标——新基准用保形概率评估不确定性保留。
arXiv:2606.01850v1 Announce Type: new Abstract: Model compression techniques such as quantization and pruning are widely used to reduce the deployment…
被ICML 2026接收，提出用选择性保形优化提升LLM成对评估的可靠性，解决打分偏差难题
arXiv:2602.13110v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as scalable judges in pairwise evaluation…
为弱语言模型群组设计的一种任意时刻有效的联邦共形RAG框架，保障每个停止时间下的统计覆盖，突破传统固定时限限制。
arXiv:2605.29139v1 Announce Type: cross Abstract: Federated Conformal RAG (FC-RAG) provides distribution-free coverage for a bandwidth-limited swarm o…
新方法融合分位数不确定性训练与保形校准，为成像逆问题提供统计可靠的误差界限，提升深度学习在医学影像中的可信度。
arXiv:2507.14760v2 Announce Type: replace-cross Abstract: While deep learning offers tremendous promise for scientific and medical imaging, any failur…
提出视觉姿态估计与运动预测框架，用保形预测实现可认证安全的人机协作。
arXiv:2604.15221v2 Announce Type: replace-cross Abstract: We propose a framework for vision-based human pose estimation and motion prediction that giv…