Falsification, Not Exposure: An Internally Preregistered Placebo-Controlled Decomposition of Self-Repair Feedback in Frozen Small Code Models
arXiv:2606.31511v1 Announce Type: cross Abstract: In deployment settings where retraining is infeasible, small frozen code models are routinely asked to repair a failed program after seeing their own failing output, usually treated as a retry mechanism. From a Popperian view, a generated program is a conjecture and a test-execution violation is an oracle-relative, executable counterexample, so feedback's value should be attributed not to re-exposure to failing code but to whether the conjecture is opened to external, executable criticism. As the third stage of a falsification-centered measurem
The proliferation of small code models in deployment settings necessitates improved self-correction mechanisms to enhance reliability and reduce manual intervention.
Improving the autonomous self-repair capabilities of frozen code models is critical for scalability and efficiency in software development and intelligent systems.
The understanding of effective feedback loops for code models shifts from mere exposure to failing output to structured, falsification-based criticism, potentially leading to more robust AI agents.
- · AI software developers
- · Companies deploying small code models
- · Automation platforms
- · AI agent orchestrators
- · Manual debugging processes
- · Inefficient AI systems
- · Legacy code repair methods
Small code models become more adept at autonomous error correction, reducing human oversight.
This capability enables more complex and reliable AI agents to operate with less intervention, expanding their application scope.
The enhanced robustness of AI-generated code could accelerate the development of fully autonomous software engineering systems.
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