🤖 AI Summary
This work addresses the prevalent issue in autonomous coding agents that prematurely declare lifecycle states—such as “DONE”—without verification during multi-step software tasks, often leading to erroneous progression. To mitigate this, the authors propose Proof-or-Stop, a model-agnostic and platform-neutral trusted control layer that strictly gates state transitions only when fresh, traceable, and mechanically verifiable evidence satisfies predefined conditions. Crucially, the approach treats agent outputs as claims pending validation rather than established facts, and explicitly distinguishes between the mere existence of review mechanisms and their role as gating criteria. Empirical evaluation demonstrates zero false “DONE” declarations across ten scenarios, successful resistance against 18 classes of tampering attacks with no false acceptances, and a reduction in hidden failure rates from 31/1800 to 2/1800 in ablation studies. Furthermore, 94.8% of issues in a corpus of 565 self-application narratives were resolved.
📝 Abstract
Autonomous coding agents increasingly execute multi-step software work, but lifecycle states such as reviewed, tested, DONE, and ready-to-merge remain claims unless supported by current evidence. We present Proof-or-Stop Lifecycle Control, a method that permits lifecycle transitions only when fresh, tracked-source-state-bound, mechanically verifiable evidence satisfies the relevant gate. The method treats agent outputs as claims rather than lifecycle state, and uses proof operationally to mean gate-admissible evidence under a stated trust model, not semantic program correctness.
We evaluate an open-source implementation through mechanism tests, a powered control-policy ablation, and operated self-application evidence. The unattended-loop engine passed 10 of 10 scenarios with zero false-DONE, and local-key receipt bundles rejected 18 tamper classes with zero false accepts. In a 9,240-cell ablation, the pre-registered A4 versus A2-prime comparison reduced visible-pass/hidden-fail amplification from 31 of 1,800 injected cells under a compute-budgeted naive loop to 2 of 1,800 under the gated loop, a 1.6 percentage-point improvement in not-amplified rate with a 95 percent confidence interval of [0.8, 2.5]. A near-compute A3 versus A4 comparison, 14 of 1,800 versus 2 of 1,800, indicates that the gain is associated with enforcing review as a lifecycle gate rather than merely adding a reviewer. The self-application corpus contains 565 stories and 1,007 review findings, with 94.8 percent resolved, plus a 68-row high/critical cross-vendor exhibit. These results support Proof-or-Stop as a model-agnostic, host-neutral control layer for deciding which autonomous-agent claims a lifecycle may act on. The evaluation is limited to one model family, 24 ablation tasks, and a self-hosted corpus.