发表机构
Weill Cornell Medicine（威尔康奈尔医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出对比扩散对齐（ConDA），在冻结预训练扩散模型上学习轻量映射，实现可识别的世界模型，并在物理和机器人系统中验证了状态与因果结构的恢复能力。
AI 中文摘要
基于扩散的世界模型能够在高维动力学系统中生成和预测轨迹，但预测准确性并不意味着其潜在坐标能够恢复底层状态变量或因果交互。我们探讨是否可以在不重新训练生成骨干网络的情况下，为冻结的预训练扩散模型配备可识别的坐标。我们证明，辅助变量非线性ICA保证可以迁移到对比扩散对齐（ConDA），该方法仅在冻结的扩散潜在表示之上学习一个轻量级对齐映射。在标准TCL/GCL假设下，对齐后的表示能够识别潜在动力学状态（直至置换和逐分量可逆变换），保留潜在动力学结构因果模型，并将滞后图恢复简化为转移雅可比稀疏性。我们将基于TCL、GCL和CEBRA的ConDA与TDRL、CaRiNG、IDOL、时间SuaVE和iVAE在物理和机器人视频系统上进行了评估。TCL和GCL实现了近乎完美的分块状态恢复和具有竞争力的滞后图恢复，包括在模拟自由落体系统中的精确恢复。在模拟双足机器人中，学习到的动力学恢复了对外部控制扰动的响应的符号和时间结构。这些结果表明，冻结的生成扩散模型可以被配备可识别、结构可解释且有助于分析干预相关动力学的坐标。
英文摘要
Diffusion-based world models can generate and predict trajectories in high-dimensional dynamical systems, but predictive accuracy does not imply that their latent coordinates recover the underlying state variables or causal interactions. We ask whether a frozen pretrained diffusion model can be equipped with identifiable coordinates without retraining its generative backbone. We show that auxiliary-variable nonlinear ICA guarantees can be transferred to Contrastive Diffusion Alignment (ConDA), which learns only a lightweight alignment map on top of frozen diffusion latents. Under standard TCL/GCL assumptions, the aligned representation identifies latent dynamical states up to permutation and componentwise invertible transformations, preserves the latent dynamic structural causal model, and reduces lagged graph recovery to transition-Jacobian sparsity. We evaluate TCL-, GCL-, and CEBRA-based ConDA against TDRL, CaRiNG, IDOL, temporal SuaVE, and iVAE across physical and robotic video systems. TCL and GCL achieve near-perfect blockwise state recovery and competitive lagged graph recovery, including exact recovery in a simulated falling-body system. In a simulated bipedal robot, learned dynamics recover the sign and temporal structure of responses to held-out control perturbations. These results show that a frozen generative diffusion model can be equipped with coordinates that are identifiable, structurally interpretable, and useful for analyzing intervention-relevant dynamics.
