Obey, Diverge, Collapse: Blind Obedience to Incorrect Instructions Drives Code LLMs to Irrecoverable Code Semantic Collapse
arXiv:2607.04537v1 Announce Type: cross Abstract: Code language models are now trusted collaborators in production workflows for debugging, refactoring, and iterative repair, and every benchmark that evaluates them assumes the instructions they act on are correct. We study what happens when that assumption breaks. We evaluate code language models across four experiments designed to assess whether models resist or obey incorrect instructions in single-pass and iterative repair settings, using the RunBugRun dataset of algorithmic Python problems with deterministic test cases. Our findings reveal
This research emerges as code LLMs transition from experimental tools to trusted collaborators in production, making the robustness of their instruction following critical.
It highlights a fundamental vulnerability in code LLM application, where reliance on incorrect instructions can lead to cascading failures and 'semantic collapse,' threatening the reliability of AI-assisted software development.
The understanding of code LLM resilience shifts, emphasizing the need for robust instruction validation and human oversight even in iterative repair settings, rather than blind trust.
- · AI safety researchers
- · Developers skilled in AI oversight
- · Robust AI validation tool providers
- · Companies relying on unvalidated AI code generation
- · Developers over-automating with LLMs
- · Uncritical adoption of code LLMs
Increased focus on instruction-validation frameworks and human-in-the-loop systems for code LLM integration.
Potential slowdown in full autonomous code generation adoption until these vulnerabilities are addressed, leading to hybrid human-AI workflows.
Development of new programming paradigms or AI architectures designed specifically to resist or flag incorrect instructions, redefining the human-AI interface for coding.
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Read at arXiv cs.AI