mlsanityData to 14 Oct 2026

Beyond Task Success: Stage-Wise Reliability of World Model Planning under Sensing Degradation

arXiv 2609.07126 ↗First posted 7 Sep 2026cs.RO cs.LGcited by 0

On the map: World models → Latent predictive world models

Abstract (arXiv)

In world model planning, sensing inputs pass through an encoder and predictor before affecting planner decisions, so final task success alone cannot reveal where sensing disturbances attenuate or persist in the pipeline. We apply 10 visual and temporal sensing degradations to a world model planner and track their effects across representation, future prediction, planner preference, and physical outcome using paired evaluation on the same 50 tasks. The relative impact of degradations was not preserved across stages: large representation shifts could attenuate downstream, while smaller initial shifts could persist to the outcome, and internal-response ordering did not directly match physical-outcome ordering. Temporal degradations also showed distinct patterns: even with similar overall changes in observation history, responses differed substantially with the location of corrupted information and the planner's actual exposure. This non-uniform stage-wise response was also observed in secondary evaluations with another manipulation task and a different world model. Stage-wise diagnosis can therefore identify where sensing disturbances attenuate or persist and help prioritize subsequent model verification and sensing mitigation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 10 of its 11 references that are in the map. First posted on arXiv 7 Sep 2026.

Builds on

10 of 11 references.

Closest ancestors · 6

Same direction, newest first

  1. 2026Diagnosing JEPA World Models with Action-Conditioned Predictive Consistency13 Aug
  2. 2026VIScore: Diagnosing Planning-Relevant Quality in Latent World Models11 Aug
  3. 2026A Control Theory of Predictability in Latent World Models11 Jul
  4. 2026LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels13 Mar
  5. 2026Learning Invariant Visual Representations for Planning with Joint-Embedding Predictive World Models20 Feb
  6. 2024DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning7 Nov

Other references · 4

  1. 2025WorldVLA: Towards Autoregressive Action World Modelcited by 367
  2. 2020Reinforcement Learning with Random Delayscited by 59
  3. 2025Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Modelcited by 36
  4. 2026stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluationcited by 20

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Close in wording, no citation either way

  1. 2026Action-Consequence Alignment for Reliable Planning and Self-Improving in Latent World Models
  2. 2026Beyond Visual Quality: A Study of Test-Time Planning with World Action Models
  3. 2026When Low Prediction Error Misleads Planning: Diagnosing Representation, Dynamics, and Decision Failures in Latent World Models
  4. 2026World Models for Embodied Intelligence: From Plausible to Controllable to Actionable
  5. 2026How Much Planning Is Enough? Reducing Search and Computation in World-Model Planning
  6. 2026Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models
  7. 2026One-Step Prediction Fidelity Is a Poor Proxy for Planning Performance in JEPA World Models
  8. 2026LIBERO-MAX: Do Robot Policies Adapt When the World Changes?