Researchers Detect Functional Memorization in Code Language Models Beyond Textual Overlap
Researchers have demonstrated that large language models trained on code can reproduce the functional logic of training examples even when the generated code looks textually different. The study used a counterfactual setup comparing a midtrained version of OLMo-3-32B against a pretrained reference model to isolate what was learned from exposure to specific code. This matters because current auditing methods that rely on textual overlap may significantly underestimate how much proprietary or sensitive code logic is recoverable from AI models.
A preprint posted to arXiv introduces the concept of 'functional memorization' in code language models, arguing that existing verbatim or textual-overlap metrics fail to capture the full extent to which training data can be extracted from model outputs. The researchers constructed a controlled counterfactual experiment using OLMo-3-32B, comparing a midtrained model that had been exposed to target code against a pretrained baseline that had not. Both models were prompted with Python function signatures, and their outputs were evaluated using both textual similarity measures and functional similarity methods, including LLM-as-a-judge and execution-based testing. Results showed clear evidence that the midtrained model reproduced functional logic from training examples even when the generated code was textually dissimilar to the original. The findings suggest that current privacy and copyright auditing frameworks for code AI systems may be inadequate, and that functional equivalence must be incorporated into memorization assessments. The work has implications for intellectual property, data privacy, and the governance of AI systems trained on large code corpora.
What's missing
The study does not detail the size or composition of the target code dataset used in the counterfactual setup, making it difficult to assess how broadly the findings generalize across different code domains or licensing types. It is also unclear whether the functional memorization detected poses practical extraction risks at scale, or how the LLM-as-a-judge evaluation method was validated for reliability. The paper has not yet undergone peer review.
What different sources said
- arXiv cs.CLCenter
Detecting Functional Memorization in Code Language Models
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