Event-Conditioned Diagnostics of Kinematic, Contact, and Object-Permanence Structure in Passive Object-State World Models
On the map: World models → Latent predictive world models
Abstract (arXiv)
World models can predict future physical states, but prediction accuracy alone does not explain how physical information is organized and used inside their latent dynamics. We introduce a controlled diagnostic protocol for studying event-conditioned latent physical structure in passive object-state world models. The protocol separates three questions: whether event-regime information is readable, whether event context changes the relative emphasis of kinematic-, contact-, and object-permanence-related readouts, and whether suppressing readout-aligned directions affects prediction. Using a balanced controlled-generator dataset with free-motion, collision, and occlusion events, we evaluate GRU, Transformer-lite, and RSSM-lite transition models under a fixed-horizon forecasting setup. Across three independent seeds, all three architectures learn useful predictive dynamics and support reliable event-regime readout. Event context systematically shifts the relative readout pattern: free motion is kinematic-dominant, collision increases contact-related structure, and occlusion increases object-permanence-related structure. Time-aligned analyses show corresponding phase-related shifts. Fixed-horizon projection Causal Field Effect (CFE), used here as a functional-sensitivity test, gives the clearest evidence for contact-aligned structure in collision-contact windows. Object-permanence-aligned structure is also sensitive during hard occlusion, but control specificity remains mixed. These results support event-conditioned latent structure and functional sensitivity without implying explicit physical modules or isolated causal circuits.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 21 of its 21 references that are in the map. First posted on arXiv 26 Jun 2026.
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21 of 21 references.
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- 2026Causal Physics Steering in Video World Models via Concept Activation Vectors23 May
- 2026Interpreting Physics in Video World Models4 Feb
- 2018Learning Latent Dynamics for Planning from Pixels12 Nov
Other references · 18
- 2018World Modelscited by 1,444
- 2023Mastering Diverse Domains through World Modelscited by 1,193
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- 2022Kubric: A scalable dataset generatorcited by 373
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- 2022SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Modelscited by 146
- 2021Physion: Evaluating Physical Prediction from Vision in Humans and Machinescited by 145
- 2019PHYRE: A New Benchmark for Physical Reasoningcited by 137
- 2019CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoningcited by 137
- 2025IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environmentscited by 49
- 2020Learning Object Permanence from Videocited by 29
- 2023Object-Centric Video Prediction via Decoupling of Object Dynamics and Interactionscited by 19
6 more
- 2022Variational Causal Dynamics: Discovering Modular World Models from Interventionscited by 16
- 2025Dynamics-Aligned Latent Imagination in Contextual World Models for Zero-Shot Generalizationcited by 5
- 2026What Do World Models Learn in RL? Probing Latent Representations in Learned Environment Simulatorscited by 4
- 2023Learning Object Permanence from Videos via Latent Imaginationscited by 3
- 2024OCK: Unsupervised Dynamic Video Prediction with Object-Centric Kinematicscited by 3
- 2026CausalVAE as a Plug-in for World Models: Towards Reliable Counterfactual Dynamicscited by 2
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