arXiv Machine Learning
Token Signatures of Code: Comparing Coding Behaviors Across Large Language Models
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Jul 3
TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models
arXiv:2607. 01235v1 Announce Type: cross Abstract: Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and practitioners.
arXiv AI
Aug 17
The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion
arXiv:2508. 16131v3 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context.
arXiv AI
Jun 9
Lost in the Flow with Code Talkers: Unveiling the Instruction-Tuning Tax of Large Language Models in Code Tasks
arXiv:2606. 08676v1 Announce Type: cross Abstract: AI coding assistants have significantly improved developer productivity by automatically suggesting code that aligns with user intent, and many of these tools are now integrated directly into Integrated Development Environments (IDEs).
arXiv AI
Aug 11
Contamination Means Overestimation? A Fine-Grained Empirical Study in Code Intelligence
arXiv:2506. 02791v4 Announce Type: replace-cross Abstract: In recent years, code intelligence has gained increasing importance in the field of automated software engineering.
arXiv Machine Learning
Jul 14
Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Understanding
arXiv:2505. 13353v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed for understanding large codebases, but whether they understand operational semantics of long code context or rely on pattern matching shortcuts remains unclear.
arXiv AI
Aug 18
Invariant Pretraining for Robust Code Representations
arXiv:2608. 15412v1 Announce Type: cross Abstract: Encoder-based code representation models remain widely deployed for discriminative tasks such as clone detection and code classification, where their small size and low inference cost are decisive.