Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts
arXiv:2606.28228v1 Announce Type: new Abstract: Causal representation learning for time series has developed strong identifiability results in discrete-time latent causal models, but identifiability in continuous-time latent stochastic differential equation (SDE) models remains largely open. We address this gap using environment-induced shifts in diffusion covariance. We study additive-noise latent SDEs observed through an unknown nonlinear diffeomorphism, with shared drift but environment-specific diffusion covariance. We show that two diagonal diffusion regimes with pairwise distinct coordin
This publication represents continued academic progress in the fundamental understanding of causal representation learning within continuous-time models, expanding beyond prior discrete-time focus.
Improved identifiability in latent SDE models is crucial for developing more robust and interpretable AI systems, particularly in dynamic and complex real-world environments.
This research provides a new theoretical foundation for disentangling latent dynamics in continuous-time AI models, potentially leading to more reliable causal inference and generalizable AI agents.
- · AI researchers
- · Developers of AI agents
- · Industries utilizing time-series data
- · Systems relying on opaque or poorly understood latent dynamics
Advances in understanding continuous-time latent dynamics will enable the development of more sophisticated and trustworthy AI systems.
This foundational work could accelerate progress in AI agent development by improving their capacity for understanding and acting on causal relationships over time.
More interpretable and causally aware AI could lead to broader adoption in high-stakes applications like healthcare and autonomous systems, increasing societal reliance on AI.
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