世界模型

AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL

AlphaClifford：基于模型强化学习的高效Clifford电路综合与 transpilation

Daniele Lizzio Bosco, Jacopo Cossio, Carla Piazza, Giuseppe Serra

专题命中 模型式强化学习 ：model-based RL(title,abstract)；model-based reinforcement learning(abstract)；分类 cs.AI

AI总结 本研究提出基于模型强化学习的AlphaClifford框架，用于高效综合Clifford电路，在总门数和CNOT门数上优于现有方法，还可用于硬件约束 transpilation及Clifford+T流程优化，为量子编译提供可扩展途径。

Comments Accepted manuscript to appear in the proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE 2026). 11 pages, 3 figures. v2: Added discussion and citation of concurrent work by Yeung, Kissinger, and Cornish ( arXiv:2605.10910 (https://arxiv.org/abs/2605.10910) )

LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models

LD4WAM：从人类视频学习世界动作模型的潜在动力学

Zhenhao Shen, Jiaqi Liang, Jasper Lu, Feng Jiang, Yuran Wang, Chuanbo Wei, Jiayi Liu, Jianchun Yang, Qize Yu, Jiadi You, Ce Hao, Guanqi He, Chen Xie, Ruihai Wu

专题命中 模型式强化学习 ：latent dynamics(title,abstract)；分类 cs.RO；dynamics model(abstract)

AI总结 LD4WAM通过运动对齐的潜在动力学，结合语义重建与真实运动对齐训练的潜在动力学模型和MoT架构的世界动力学动作模型，在RoboTwin仿真及真实机器人上表现良好，可泛化到未见物体与背景。