Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making

2026-05-15

AI Summary

This work addresses a critical limitation in existing diffusion-based decision-making approaches, which often neglect the evolving latent dynamics of environments, thereby struggling to accurately model state transitions, reward structures, and high-level behaviors. To overcome this, the paper proposes the first unified causal diffusion framework with theoretical identifiability guarantees, capable of explicitly inferring and jointly learning latent dynamics and observation interactions from limited data. By integrating a modular architecture with a novel identification technique based on short temporal segments, the method adaptively captures changes in dynamics, rewards, and latent actions. Empirical evaluations on simulated control and robotic benchmark tasks demonstrate substantial improvements in both latent inference accuracy and policy generalization.

Abstract

Recent work has framed decision-making as a sequence modeling problem using generative models such as diffusion models. Although promising, these approaches often overlook latent factors that exhibit evolving dynamics, elements that are fundamental to environment transitions, reward structures, and high-level agent behavior. Explicitly modeling these hidden processes is essential for both precise dynamics modeling and effective decision-making. In this paper, we propose a unified framework that explicitly incorporates latent dynamic inference into generative decision-making from minimal yet sufficient observations. We theoretically show that under mild conditions, the latent process can be identified from small temporal blocks of observations. Building on this insight, we introduce Ada-Diffuser, a causal diffusion model that learns the temporal structure of observed interactions and the underlying latent dynamics simultaneously, and furthermore, leverages them for planning and control. With a modular design, Ada-Diffuser supports both planning and policy learning tasks, enabling adaptation to latent variations in dynamics, rewards, and latent actions. Experiments on simulated control and robotic benchmarks demonstrate its effectiveness in accurate latent inference and adaptive policy learning.

Problem

• latent dynamics
• decision-making
• diffusion models
• sequential modeling
• adaptive control

Innovation

• latent dynamics
• adaptive diffusion
• decision-making
• causal diffusion model
• sequence modeling