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Event-Conditioned Diagnostics of Kinematic, Contact, and Object-Permanence Fields in Passive Object-State World Models

This paper introduces a controlled diagnostic protocol to study event-conditioned latent physical structure in passive object-state world models. Using a balanced dataset with free-motion, collision, and occlusion events, the authors evaluate recurrent, attention-based, and latent state-space models. Results show that hidden states encode event-regime information, event contexts reweight physical fields, and field-aligned directions have functional consequences for prediction.

SourcearXiv RoboticsAuthor: Yang Liu, Yuming Chen

[2606.28455] Event-Conditioned Diagnostics of Kinematic, Contact, and Object-Permanence Fields in Passive Object-State World Models

[Submitted on 26 Jun 2026]

Title:Event-Conditioned Diagnostics of Kinematic, Contact, and Object-Permanence Fields in Passive Object-State World Models

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Abstract: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 tests whether hidden representations encode event-regime information, whether event contexts reweight non-exclusive physical field readouts, and whether field-aligned representational components have functional consequences for prediction. Using a balanced controlled-generator dataset with free-motion, collision, and occlusion events, we evaluate recurrent, attention-based, and latent state-space transition models under a fixed-horizon forecasting setup. The models learn useful predictive dynamics and their hidden states support reliable event-regime readout. Event contexts systematically reweight kinematic, contact, and object-permanence field readouts: free motion is kinematic-dominant, collision combines kinematic and contact structure, and occlusion combines motion-related and object-permanence structure. Time-aligned and directional-consistency analyses further show phase-related shifts in field emphasis. Finally, fixed-horizon projection causal field effect (CFE) shows that suppressing field-aligned directions can degrade event-relevant prediction, with strongest evidence for contact-aligned structure in collision-contact windows and more qualified evidence for object-permanence-aligned structure in hard-occlusion hidden windows. These results support event-conditioned organization and fixed-horizon functional sensitivity of latent physical fields, while not implying explicit physical modules, isolated causal circuits, or context-invariant sliding-window generalization.

Subjects:

Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

ACM classes: I.2.m

Cite as: arXiv:2606.28455 [cs.RO]

(or arXiv:2606.28455v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.28455

arXiv-issued DOI via DataCite

Related DOI:

https://doi.org/10.5281/zenodo.20827855

DOI(s) linking to related resources

Submission history

From: Yang Liu [view email] [v1] Fri, 26 Jun 2026 10:11:59 UTC (982 KB)

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