TRACER: A Semantic-Aware Framework for Fine-Grained Contamination Detection in Code LLMs
Data contamination is a known threat to the reliability of model evaluation. However, it remains underexplored in code large language models (LLMs), where contamination often goes beyond exact duplication. We present TRACER, a semantic-aware framework for fine-grained code contamination detection. TRACER models contamination using three levels of semantic overlap - Functionally Identical, Nearly Identical, and Shared Logic - and detects them through a coarse-to-fine pipeline. We also introduce the first benchmark for fine-grained code contamination detection, spanning three widely used benchmarks and three representative post-training datasets. TRACER achieves strong and consistent perfor...
| Authors | Yifeng Di, Xuliang Huang, Tianyi Zhang |
|---|---|
| Primary category | cs.SE |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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