arXiv:2509.12263v3 Announce Type: replace-cross Abstract: Large multimodal models (LMMs) encode physical laws observed during training, such as momentum conservation, as parametric knowledge. It allows LMMs to answer physical reasoning queries, such as the outcome of a potential collision event from visual input. However, since parametric knowledge includes only the physical laws seen during training, it is insufficient for reasoning in inference scenarios that follow physical laws unseen during training. In such novel physical environments, humans could adapt their physical reasoning based on
The rapid advancement and deployment of Large Multimodal Models (LMMs) are leading to a deeper understanding of their current limitations, especially in tasks requiring inductive reasoning beyond trained parametric knowledge.
This research highlights a significant current constraint of leading AI models, indicating that advanced physical reasoning, particularly in novel situations, still requires breakthroughs beyond current training paradigms.
The understanding of LMM capabilities is refined, suggesting that their 'physical intuition' is more rote memorization of observed laws than true inductive reasoning, impacting expectations for autonomous systems in unpredictable environments.
- · Researchers in inductive reasoning and causality
- · Developers of simulation environments for novel scenarios
- · Companies focusing on hybrid AI approaches
- · Developers relying solely on scaling current LMM architectures for physical inte
- · Applications requiring robust physical reasoning in unseen conditions without hu
LMMs currently lack true inductive physical reasoning, struggling with scenarios outside their training data.
This limitation will necessitate new architectural approaches or hybrid AI systems to achieve robust physical intelligence in general-purpose AI applications, especially in robotics.
The pursuit of inductive physical reasoning could accelerate breakthroughs in causal AI and general intelligence, moving beyond pattern recognition towards more human-like understanding of the world.
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