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.
By Yifeng He, Yundi Xu, Christopher Castro Gaw Gonzalo, Zili Wang, Hao Chen
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
arXiv:2608. 07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs.
By Calvin Higgins, Marco Alvarez
arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.
By Ankit Gupta, Aditya Prasad, Rameswar Panda
The paper introduces Identifier-Free Code Embedding Models designed to improve function association for binary reverse engineering. It formalizes the bidirectional association problem between source code and decompiled, stripped code, and evaluates embedding models on this task. By fine‑tuning a Qwen3‑Embedding model with contrastive learning, the authors achieve significant performance gains over existing baselines and demonstrate generalization to a constant‑algorithm association task.
By Eric Wolos, Michael Doyle
arXiv:2606. 18286v1 Announce Type: new Abstract: Supervised fine-tuning of code LLMs typically applies uniform cross-entropy loss to all response tokens, implicitly assuming that every token provides equally useful learning signal.
By Zhijie Deng, Ling Li, Jinlong Pang, Kaiqin Hu, Qi Xuan, Zhaowei Zhu, Jiaheng Wei