Code Correctness Signals in LLM Hidden States: Pre-Generation Probing and Repair Geometry
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Computer Science > Machine Learning
Title:Code Correctness Signals in LLM Hidden States: Pre-Generation Probing and Repair Geometry
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 linear effect of prompt length is removed from each hidden state dimension, the probe still reaches 0.911 +/- 0.010, well above a prompt-length baseline of 0.754 +/- 0.014. Second, on 236 cleaned cases where the model attempts to repair a failed first attempt, the hidden state shift from the failing attempt to its repair carries a statistically detectable contrastive direction, significant on both a magnitude and a split-half test against label-shuffled nulls. This direction does not survive a conditional residualization against repair-context covariates that differ between successful and failed repairs, marking it as a correlate of repair success driven by the repair context rather than an isolated repair-comprehension feature. The probe layer is selected by nested cross-validation, and the same residualization approach that upholds the pre-generation correctness result overturns the repair-direction interpretation. The contribution is as much methodological as empirical: a diagnostic honest enough to report a negative result alongside a positive one.
| Comments: | 12 pages, 8 tables. Code, data, and analysis scripts available at this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.14530 [cs.LG] |
| (or arXiv:2606.14530v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14530
arXiv-issued DOI via DataCite (pending registration)
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