Self-Authored Verification Is Unreliable in Heuristic Self-Improving Agents

2026-07-27

AI Summary

This work addresses the performance degradation often observed in self-improving agents due to a misalignment between self-generated validation signals and actual deployment performance. To mitigate this issue, the authors propose the Sealed External Audit Loop (SEAL) mechanism, which preserves the agent’s ability to generate its own test cases while incorporating an external audit signal that the agent cannot manipulate. SEAL enforces a fixed audit loop, compares candidate policies, conducts sealed evaluations, and maintains state consistency to effectively block harmful updates. Experimental results across six models and three random seeds demonstrate that SEAL significantly outperforms unprotected baselines, robustly alleviating self-validation failure—particularly in capability-stratified agents.

Abstract

Self-improving agents accumulate capability by repeatedly rewriting procedural policies, controllers, or heuristic rules. They typically rely on self-authored tests or metrics to decide whether to accept subsequent edits. The agent controls both the optimized object and its verifier. As a result, self-assigned scores can remain near perfect while real deployment performance degrades or stays low. We study this problem through the verifier--deployment gap. This gap refers to the discrepancy between an agent's self-authored verification signal and a sealed deployment evaluation that the agent cannot observe or access. We ask how self-authored verification fails under iterative policy-and-test rewriting, how the failure changes with capability, and how little exogenous trust is sufficient to prevent real regressions from being deployed. To address this problem, we introduce a Sealed Exogenous Acceptance Loop (SEAL). SEAL retains self-authored tests but compares each candidate with the incumbent through a fixed harness-side audit. The agent cannot author or inspect the audit, receives only accept/reject, and the whole incumbent state is retained after a clear regression. Our experiments show that this problem often appears in heuristic learning settings. These settings require trial-and-error discovery of the target objective. We further find that failures of self-written verification are stratified by capability. Weaker agents tend to damage previously acquired strategies behind easy self-tests. Stronger agents are more stable, but they still mismeasure the deployment distribution. Standard self-written constraints do not reliably close this gap. In contrast, SEAL outperforms unprotected baselines across six models and three random seeds. Reliable self-improvement need not abandon self-verification, but it requires at least one deployment-acceptance signal outside the agent's control.

Problem

• self-improving agents
• self-authored verification
• verifier-deployment gap
• heuristic learning
• deployment performance

Innovation

• self-improving agents
• verification-deployment gap
• Sealed Exogenous Acceptance Loop
• heuristic learning
• exogenous audit