Mechanisms of Misgeneralization in Physical Sequence Modeling

2026-05-22 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Engineering & Applied Sciences · Depth: Expert, extended

Summary

Generative sequence models, widely used in robotics and mechanical simulations, often fail to reproduce the intended aggregate distribution of physical quantities such as travel distance or mechanical energy, a phenomenon termed "physical misgeneralization." Researchers from Harvard, Comcast AI, NTT Research, and Microsoft identified that local errors, typical of the model class, propagate through physical measurements to shift the recovered distribution. They developed a "data deviation kernel" to estimate these errors, accurately predicting which physical quantities gain or lose mass. Experiments involved training 1D U-Net diffusion models on 200,000 trajectories with 256 DDPM steps, validating the mechanism across synthetic tasks, Maze2D navigation (path length range [3.80, 4.65]), and double-pendulum motion (mechanical energy range [5, 40]).

Key takeaway

For Machine Learning Engineers developing generative sequence models for physical systems, recognize that models can produce plausible individual trajectories but fail to preserve aggregate physical quantity distributions. You should use the proposed data deviation kernel to anticipate these "physical misgeneralization" shifts early in development. Consider transforming your data representation based on kernel insights, as this mitigation consistently reduces drift more effectively than conditional modeling or dataset rebalancing.

Key insights

Local model errors propagate through physical measurements, causing generative models to misrepresent aggregate physical quantity distributions.

Principles

• Generative models can fail to preserve aggregate physical distributions.
• Local model errors propagate through physical measurements.
• Data deviation kernels predict quantity shifts a priori.

Method

Estimate local model errors with a data deviation kernel, then propagate these perturbations through the physical measurement rule to predict quantity distribution shifts.

In practice

• Transform data representation using kernel-derived coordinate systems.
• Avoid simple dataset rebalancing for complex physical quantities.

Topics

• Physical Misgeneralization
• Generative Sequence Models
• Data Deviation Kernel
• Robotics
• Dynamical Systems
• Diffusion Models

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.