COCORELI: Enforcing Execution Preconditions for Reliable Collaborative Instruction Following

Abstract:Autonomous agents executing human instructions must operate reliably even when instructions are incomplete. While recent approaches improve detection of missing information, detection alone is insufficient: agents often proceed to execution even after recognizing underspecification, leading to incorrect or unsafe actions. We identify this failure as arising from a lack of coupling between detection and execution, and propose that reliable behavior requires enforcing missing information as a precondition for action. We instantiate this principle in Cocoreli, a modular architecture that represents task structure, tracks missing information, and blocks execution until required details are resolved through targeted clarification. In Cocoreli, detection and prevention are structurally coupled: detecting a missing parameter simultaneously blocks execution. We evaluate Cocoreli in a controlled construction environment isolating underspecification and sequential execution. Cocoreli blocks execution under unresolved specifications by construction, eliminating hallucinated actions. In contrast, chain-of-thought, prompt-chaining, and ReAct-style reasoning may still execute under incomplete specifications despite high detection rates. The same representation supports abstraction and reuse, and generalizes to API workflow tasks on ToolBench. These results show that reliable collaborative execution requires architectural enforcement, not just model capability

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2509.04470 [cs.CL]

(or arXiv:2509.04470v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2509.04470 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.18653/v1/2026.sigdial-1.36 Focus to learn more DOI(s) linking to related resources

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