The paper introduces LWVIC4Code, a non‑contrastive, layer‑wise representation learning method for detecting Type‑IV code clones—semantically equivalent fragments that differ syntactically. It builds on the VICReg framework, adding cross‑layer consistency regularization and depth‑dependent weighting to refine semantic information across transformer layers. Experiments on Python and multi‑language datasets show that LWVIC4Code matches or outperforms contrastive baselines and zero‑shot large language models, generalizing well to Java and C# without requiring negative samples.
By Luciano Marchezan, Kevin Delcourt, Eugene Syriani, Houari Sahraoui
The paper investigates using synthetic natural-language descriptions to contrastively pretrain small transformer encoders for code representation. By pairing generated descriptions with code in a dual-encoder setup during training and discarding them at inference, the authors achieve significant improvements over traditional pretraining baselines on most evaluated tasks. When fine‑tuned, these models match or surpass much larger zero‑shot models and remain competitive with execution‑aware supervision, indicating a scalable alternative for code embeddings.
By Kenneth Paulsen, Florian Tambon, Mike Papadakis, Shin Yoo
The paper investigates whether code large language models (CodeLLMs) inadvertently reproduce proprietary or sensitive code by evaluating seven state‑of‑the‑art training data detection (TDD) methods on eight CodeLLMs. It introduces CodeSnitch, a benchmark of 9,000 function‑level code samples across three languages, each labeled as included or excluded from training data, and applies mutation strategies based on the Type‑1 to Type‑4 code clone taxonomy to test TDD robustness. The study offers a systematic assessment of current TDD techniques for code and suggests directions for developing more effective detection methods.
By Tianlin Li, Yunxiang Wei, Zhiming Li, Aishan Liu, Qing Guo, Xianglong Liu, Dongning Sun, Yang Liu
arXiv:2607. 23271v1 Announce Type: cross Abstract: Contrastive vision-language models such as CLIP map semantically opposite phrases (e.
By Chen-Yi Lu, Yueh-Shao Chen, Somali Chaterji
arXiv:2607. 14113v1 Announce Type: cross Abstract: While many AI-generated text (AIGT) detectors achieve strong performance on clean inputs, their accuracy degrades significantly under light paraphrasing, word substitutions, character edits, and distribution shifts.
By Gayan K. Kulatilleke, Mahsa Baktashmotlagh, Siamak Layeghy, Marius Portmann
arXiv:2606. 24716v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy metrics or qualitative inspection rather than measuring semantic correspondence.
By Jonas Klotz, Cassio F. Dantas, Pallavi Jain, Diego Marcos, Beg\"um Demir