Dual-Frontier: When Can an Agent Trust Its World Model?
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Merged summary
TL;DR - Dual-Frontier is a framework for deciding when an agent can safely act on predictions from a learned world model. It addresses the otherwise unidentifiable question of whether failures stem from the agent’s decision rule or inaccurate model predictions.
- Proves that decision error and world-model error cannot be disentangled from passive interaction trajectories, even with finite-horizon planning.
- Admits a model-guided decision only when its predicted advantage exceeds a certified bound on decision-relevant model error; otherwise, it prioritizes model verification.
- Provides action-conditioned value bounds and a closed-loop extension guaranteeing non-decreasing return for admitted decisions.
- Experiments with learned models and cross-backbone tool-use benchmarks report improved decision quality and reliability from this verify-then-promote approach.
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Dual-Frontier: When Can an Agent Trust Its World Model?
Public signals
N/A
TL;DR - Dual-Frontier is a framework for deciding when an agent can safely act on predictions from a learned world model. It addresses the otherwise unidentifiable question of whether failures stem from the agent’s decision rule or inaccurate model predictions.
- Proves that decision error and world-model error cannot be disentangled from passive interaction trajectories, even with finite-horizon planning.
- Admits a model-guided decision only when its predicted advantage exceeds a certified bound on decision-relevant model error; otherwise, it prioritizes model verification.
- Provides action-conditioned value bounds and a closed-loop extension guaranteeing non-decreasing return for admitted decisions.
- Experiments with learned models and cross-backbone tool-use benchmarks report improved decision quality and reliability from this verify-then-promote approach.