arXiv:2606.14530v1 Announce Type: new Abstract: Large language models encode rich information in their hidden states. This work asks whether code correctness is legible in the hidden states of Qwen3-4B-Instruct-2507, before it generates and as it repairs a failed attempt, studied on 444 LiveCodeBench tasks. It reports two findings connected by a single confound-control tool: residualization. First, the correctness of the model's first-attempt code is linearly decodable from the prompt-final hidden state, with a leakage-free held-out AUC of 0.931 +/- 0.008 across 50 outer splits. After the line
The rapid advancement and deployment of large language models for code generation necessitate deeper understanding and control of their internal mechanisms for reliability and performance.
This research provides a mechanism to predict and improve the correctness of AI-generated code before deployment, enhancing the efficiency and trustworthiness of AI in software development.
The ability to 'read' an LLM's internal state for code correctness offers a new paradigm for quality assurance in AI-assisted programming, moving beyond post-generation testing.
- · AI developers
- · Software engineers
- · Companies using LLMs for code
- · AI safety researchers
- · Manual code debuggers
- · Companies relying solely on post-generation testing
Increased efficiency and reliability in AI-assisted code generation and repair pipelines.
Accelerated development cycles for new software and systems that leverage AI for foundational code.
The development of highly robust and autonomous AI agents capable of self-correcting complex programming tasks with minimal human oversight.
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Read at arXiv cs.LG