ResearchResearch paperRobotics & Embodied AI · Large Language Models · Interpretability1 source · Oct 19, 2026

Meta-iLaD: Identifiable Latent Dynamics via Meta-Learning of Dynamics Environments

We address these limitations with Meta-iLaD, a novel identifiable latent dynamics framework attained by meta-learning across dynamics environments.

Learning latent dynamics is central to assessing the current states and forecasting the future trajectories of high-dimensional time series.
For locally-stationary latent dynamics $\mathcal{F}(z{<t}; c)$ with latent dynamics state $zt$ and environment variable $c$, prior identifiability results have largely focused on $zt$ when conditioned on predefined label $u$ of dynamics environments.
Meta-iLaD introduces a novel conditional prior of $c$, modeled as a feedforward meta-learner to rapidly extract $c$ from few-shot examples.
On synthetic and real data, we provide strong empirical evidence that 1) conditioning on few-shot examples enables generalization to out-of-distribution environments, and 2) identifiability for $c$ and $\mathcal{F}$ is critical for accurate forecasting beyond reconstructing observed trajectories.

[1]Meta-iLaD: Identifiable Latent Dynamics via Meta-Learning of Dynamics Environments
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:12 PM
We address these limitations with Meta-iLaD, a novel identifiable latent dynamics framework attained by meta-learning across dynamics environments.
Learning latent dynamics is central to assessing the current states and forecasting the future trajectories of high-dimensional time series.

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