arXiv:2607.01537v1 Announce Type: new Abstract: Certified world models estimate how long their predictions remain valid. We turn this validity horizon into an operational sensing clock: a rule for when an agent should stop coasting and re-sense. Starting from an audited equivariant world model, we derive a deadline for no-sensing intervals and show that deployable deadlines in learned world models must be drift-aware: on-manifold Lyapunov rates alone overestimate coasting validity, while calibrated native rollout-drift envelopes carry the deployed guarantee. On a frozen 3D VN-JEPA model, the r
The continuous advancements in AI model robustness and the increasing need for autonomous agents in real-world, dynamic environments make this a timely development for practical deployment.
This research introduces 'sensing clocks' that provide verifiable deadlines for AI perception, enabling more reliable and efficient autonomous systems by intelligently managing sensor usage and prediction validity.
AI systems can now be designed with a quantifiable understanding of when their internal world models become unreliable, moving from heuristic sensing to 'drift-aware' deadlines for active perception.
- · Autonomous systems developers
- · Robotics industry
- · AI safety researchers
- · Logistics and defence sectors
- · Systems relying on constant, unoptimized sensing
- · Less robust AI perception models
Improved efficiency and reliability of autonomous agents by optimizing sensing intervals.
Accelerated deployment of AI in mission-critical applications requiring high levels of assurance.
Reduced energy consumption and operational costs for AI-driven robots and vehicles due to judicious sensor usage.
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Read at arXiv cs.LG