University of California, Berkeley（加州大学伯克利分校）

CADENCE: Predicting Realized MAPF Execution Time Beyond Sum of Costs

CADENCE：预测实际MAPF执行时间超越成本总和

Abhishek Seetharamu, Badrikanath Praharaj, Sreeram MV

机构 * University of California, Berkeley（加州大学伯克利分校） 纠错

AI总结 提出CADENCE框架，通过分析原始运动负担和交互感知协调特征，发现原始运动负担能显著提高多智能体路径规划执行时间的预测精度，超越传统成本总和指标。

Comments 7 pages, 4 figures, 3 tables and this paper was accepted at Multi-Agent Robotic Systems: Real-World Collaboration and Interaction a workshop at the international conference of robotics and automation (ICRA 2026)

When Algorithms Meet Artists: Semantic Compression and Stake-holder Marginalisation in Public AI-Art Discourse (2013-2025)

当算法遇见艺术家：在公共AI艺术辩论中艺术家关切的语义压缩

Ariya Mukherjee-Gandhi, Oliver Muellerklein

机构 * University of California, Berkeley（加州大学伯克利分校） 纠错

AI总结 研究发现艺术家关切在公共AI艺术讨论中存在严重的语义压缩，提出基于共识的语义投影方法以解决代表缺口问题。

Comments 43 pages