AI & ML
Call-Level Revision Contracts for LLM-Authored Workflows
arXiv cs.SE0 views
arXiv:2611.06967v1 Announce Type: new Abstract: Durable execution engines replay a recorded history and do not compare activity payloads. When a model repairs a workflow that failed partway through, it must either pay again for calls that succeeded or accept a recorded result whose payload changed. We define a revision contract for workflows whose orchestration code is written by a model. Each capability call carries a structural identity built from its callsite, its fanout position, and its occurrence count, and the identity stays stable under rewrites that leave the call in place, so committed identities form a frozen prefix. The frozen unit is the call expression, and the surrounding code stays writable. A static gate rejects a rewrite that would change a committed call before a container starts. A runtime digest stops a changed payload at a committed identity, and an occurrence ledger names a repeated paid payload. Under three assumptions, a candidate the gate accepts replays every committed call it invokes, executes only new work, and reports the identities it skips. No local identity is both relocation-invariant and free of wrong reuse, so callsite identity turns a relocation into a pre-spend stop. On media workflows, 22 gate-passed repairs replay 258 committed identities with no duplicate, and a live six-way fanout makes seven paid calls instead of twelve. A 12-task customer-support study applies the same identity, replay, and boundary checks on two models.
Source:https://arxiv.org/abs/2611.06967PreviousWhen Can Self-Play Fix a False Belief? Answer Keys, Pass Bands and Replacement Rules in an Execution-Grounded LoopNextAn LLM-Orchestrated Neuro-Symbolic Framework for Verifying Diagnostic Signal Reachability in Electronic Systems
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