On the Robustness of LLMs' Internal Representation of Code Correctness
Code generated by modern language models often reads naturally. Yet, it also often fails to implement what was asked. This should be no surprise, as research shows the models' own confidence signals are poorly calibrated with actual correctness. A promising way to assess correctness looks inside the model: by contrasting the hidden states of correct and incorrect programs, recent work captured an internal signal of code correctness that is able to judge candidate solutions better than the model's token-level or stated confidence, with no test execution. However, this signal was captured under one particular way, leaving open an important question: whether it reflects a robust property of ...
| Authors | Francisco Ribeiro, Sohaila Abdulsattar, Renata Gonzalez et al. |
|---|---|
| Primary category | cs.SE |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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