Describe-Then-Act: Proactive Agent Steering via Distilled Language-Action World Models
Deploying safety-critical agents requires anticipating the consequences of actions before they are executed. While world models offer a paradigm for this proactive foresight, current approaches relying on visual simulation incur prohibitive latencies, often exceeding several seconds per step. In this work, we challenge the assumption that visual processing is necessary for failure prevention. We show that a trained policy's latent state, combined with its planned actions, already encodes sufficient information to anticipate action outcomes, making visual simulation redundant for failure prevention. To this end, we introduce DILLO (DIstiLLed Language-ActiOn World Model), a fast steering la...
| Authors | Massimiliano Pappa, Luca Romani, Valentino Sacco et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 March 2026 |
| Last updated at source | 28 August 2026 |
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