We find out: the agent's policy state is enough to estimate the future action outcome!
we'll present #ECCV2026 in Sweden.
With our plain #LLM (dubbed DILLO), the agent finds it out 14x faster.
Performance improves up to 15pp.
This is #SafeAI for #agents
arxiv.org/abs/2603.23149
arxiv.org
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 relyin...