Imagined Rollouts are Kinematic, Not Dynamic: A Diagnosis of Long-Horizon World-Model Failure
On the map: World models → Latent predictive world models
Abstract (arXiv)
Long-horizon failure in world models is conventionally attributed to compounding error, a generic framing that does not distinguish what kind of error compounds. We propose a kinematic-vs-dynamic reframing: world models tend to imagine kinematically rather than dynamically. We operationalize this as the imagined Kinematic-Consistency Error, a per-step diagnostic that measures how far a rollout departs from a closed-form kinematic null, paired with a perturbation protocol that tests whether iKCE responds when physical conditions cross a regime boundary. We instantiate the diagnostic on a released DreamerV3 checkpoint trained on DMC walker-walk, where imagined iKCE runs roughly two orders of magnitude above that of matched real-physics rollouts. Across a friction sweep that crosses the gait-collapse boundary, the model's iKCE stays statistically flat even as the trained policy's reward collapses through the same range, providing the kinematic-not-dynamic signature. The diagnostic distinguishes kinematic from dynamic imagination at horizons longer than the embodiment's gait period.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 8 of its 13 references that are in the map. First posted on arXiv 7 Jul 2026.
Builds on
8 of 13 references.
References · 8
- 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 618
- 2024Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllabilitycited by 337
- 2023DriveDreamer: Towards Real-world-driven World Models for Autonomous Drivingcited by 258
- 2023Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenescited by 191
- 2026R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentationcited by 10
- 2026StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Drivingcited by 5
- 2026Lost in Fog: Sensor Perturbations Expose Reasoning Fragility in Driving VLAscited by 3
- 2026EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Drivingcited by 2
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Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
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- 2026IMPLY: Physically Anchored Consistency for World-Model Rollouts
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- 2026Beyond Task Success: Stage-Wise Reliability of World Model Planning under Sensing Degradation
- 2026Causal-History Test-Time Scaling for Failure Recovery in Autoregressive World-Action Models
- 2026From Generative Engines to Actionable Simulators: The Imperative of Physical Grounding in World Models