arXiv:2606.28438v1 Announce Type: cross Abstract: Recursive self-training can degrade neural generative models when generated data is reused without fresh human data or external quality control. We study this risk in code LLMs, where AI-generated code can enter real repositories, later become training data, and create a repository-scale self-training loop. While software development traditionally interrupts this loop through pull-request review, tests, compilation, and human approval, AI coding tools now produce code faster than humans can review it, and code review itself is increasingly auto
The proliferation of AI coding tools has reached a critical mass where AI-generated code is entering repositories faster than human oversight mechanisms can process it, leading to potential recursive self-training loops.
This phenomenon highlights a critical vulnerability in the development and integrity of future software, as AI-generated errors or inefficiencies could become self-perpetuating within training data, degrading model performance over time.
The traditional software development lifecycle, particularly code review and quality control, is being challenged and potentially undermined by the speed and scale of AI code generation, necessitating new paradigms for verification.
- · AI verification and validation tool developers
- · Human software architects and reviewers specializing in AI code oversight
- · Companies investing in robust, external quality assurance for AI-generated code
- · Organizations relying solely on unvetted AI-generated code
- · Developers of foundational code LLMs without mechanisms to filter out undesirabl
- · Software quality that lacks continuous human intervention
AI models trained on progressively degraded code will produce lower quality or less secure outputs.
The increasing unreliability of AI-generated code will necessitate greater human oversight, potentially slowing down development cycles despite initial AI speed gains.
A 'garbage in, garbage out' scenario at scale could lead to a 'model collapse' across various AI applications, not just code, if underlying data integrity is compromised.
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Read at arXiv cs.AI