Commitment Hierarchies under Intent Revision: A Belief-Revision Account of Salvage in Tool-Use Agents

Abstract:When a user changes their mind partway through a task, an agent that has already split the task into sub-goals and paid for tool calls must decide, per cached sub-result, whether to keep, patch, or discard it (salvage), restarting wastes valid work and continuing unchanged answers the old question. Our main finding is that salvage quality is a matter of role design rather than model capability: a language model asked the keep/patch/discard question one node at a time is unreliable, but asked to classify the revision once, with a deterministic layer propagating the decision, it reaches the cost-optimal oracle on all three models tested, from two vendors. Modeling the plan as a commitment hierarchy and the intent change as a belief-revision operator with AGM style postulates, we prove that no policy observing only a node's local view can be both safe and cost optimal, while the single classification design is both. Across three environments the policy recovers the full achievable savings, 43% cheaper than restart, at 100% correctness.

Artificial Intelligence (cs.AI)

arXiv:2609.37453 [cs.AI]

(or arXiv:2609.37453v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.37453 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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