# Ontology-Grounded World Models for Failure Diagnosis and Closed-Loop Repair in Physical AI Systems

arXiv: https://arxiv.org/abs/2608.13901 · HTML: https://mlsanity.com/paper/2608.13901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 15 of 15)

- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [Revisiting Feature Prediction for Learning Visual Representations from Video](https://mlsanity.com/paper/2404.08471/) (2024)
- [RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning](https://mlsanity.com/paper/2409.14674/) (2024)
- [Geometric Action Model for Robot Policy Learning](https://mlsanity.com/paper/2606.17046/) (2026)
- [A Unified Framework for Real-Time Failure Handling in Robotics Using Vision-Language Models, Reactive Planner and Behavior Trees](https://mlsanity.com/paper/2503.15202/) (2025)
- [EV-WM: Event-Verified World Models for Long-Horizon Robotic Manipulation](https://mlsanity.com/paper/2606.13053/) (2026)
- [RoboSherlock: Cognition-enabled Robot Perception for Everyday Manipulation Tasks](https://mlsanity.com/paper/1911.10079/) (2019)

## Abstract (arXiv)

EV-WM represents candidate quality with feature and event scores, but these scores do not explicitly record an unmet task predicate, a route label for an available correction mechanism, or a post-correction acceptance result. We present Onto-EV-WM, an ontology-grounded diagnosis and verification-gated correction interface layered above EV-WM rather than a replacement world-model architecture. The implemented task-local TBox defines entity types, predicate signatures, and constraints; source-specific grounding maps predicted or simulator-observed states to task ABoxes; and deterministic rules retain each missing predicate and its arguments when assigning a route label. Learned or heuristic proposers remain separate from this symbolic interface; native task predicates determine acceptance, and the bounded protocol determines whether a failed verification is retried. In the aligned PointMaze evaluation, EV-WM and Onto-EV-WM both report 94% success, with mean final-state distances of 0.90573 and 0.61177, respectively; the separately budgeted search reaches 100% success. On LIBERO-Goal, the ontology represents failed task conditions as typed records, retains their predicate arguments, and associates them with the declared source/joint correction route and predicate-gated acceptance; the complete configuration reports 93.8% corrected-window success on seed 0 and 94.05 +- 0.30% across four evaluation-sampling seeds. On the fixed 10,030-task LIBERO-Plus registry, Onto-EV-WM succeeds on 8,526 tasks (85.00%), with suite-level success rates of 65.98% for LIBERO-10, 91.39% for LIBERO-Goal, and 91.38% for both LIBERO-Object and LIBERO-Spatial. These numbers report the performance of the complete ontology-grounded configurations under the tested simulator protocols; an ontology-only causal share is not measured separately, and real-robot recovery is not evaluated.