arXiv:2605.28880v1 Announce Type: new Abstract: Extending discrete-time causal Prior-data Fitted Networks for time series to continuous time invites writing the mechanism as a stochastic differential equation (SDE) -- but if the SDE is integrated \emph{once per observation gap}, the trajectory law depends on when it is observed, and the prior remains a discrete-time Markov model in SDE clothing. We propose a precise continuity criterion -- trajectory-law invariance to the observation schedule -- together with a three-tier taxonomy (discrete; naive observation-grid integration; fine-grid integr
The paper addresses a fundamental challenge in extending discrete-time causal models to continuous time, which is critical for developing more robust and generalizable AI systems that interact with dynamic real-world environments.
Causal foundation models are a significant step towards more advanced AI, and addressing the continuous-time aspect makes them applicable to a wider range of complex, time-dependent problems, moving beyond current discrete-time limitations.
This research introduces precise criteria and a taxonomy for continuous-time causal models, potentially enabling AI systems to reason more accurately about causality in dynamic systems, from physics to biology.
- · AI researchers
- · Robotics
- · Scientific computing
- · Autonomous systems development
- · Traditional discrete-time modeling approaches
- · Systems heavily reliant on observation-schedule dependent models
Improved understanding and modeling of causal relationships in continuous physical and biological systems.
Development of next-generation AI agents and autonomous systems that can infer causality in real-time, dynamic environments.
Accelerated scientific discovery and engineering breakthroughs by enabling AI to assist in complex, continuous-time predictions and interventions.
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Read at arXiv cs.LG