arXiv:2606. 03657v1 Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data.
By Jinnuo Liu, Yue Peng, Jinhan Niu, Hongyi Wen
The paper introduces PriCoder, a method for teaching large language models (LLMs) to effectively use private library APIs for code generation. PriCoder synthesizes training data by constructing a graph and applying two operators—Progressive Graph Evolution to increase diversity and Multidimensional Graph Pruning to enhance quality. Experiments on three mainstream LLMs demonstrate that PriCoder boosts private‑library code generation by over 20% in pass@1, while leaving general code generation largely unchanged.
By Yitong Zhang, Chengze Li, Ruize Chen, Guowei Yang, Xiaoran Jia, Yijie Ren, Jia Li
arXiv:2609.39765v1 Announce Type: new
Abstract: Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine evide...
By Xiaoqiang Wang, Bang Liu
arXiv:2607. 18642v1 Announce Type: new Abstract: Mined code corpora are abundant but uncontrolled: a snippet's semantics, surface "messiness," and difficulty are whatever the wild contained; there is no known-optimal reference to grade against; and any public sample may already sit in a model's training set.
By Yuxiang Ji
arXiv:2609.01601v1 Announce Type: cross
Abstract: The repository-level code generation task requires synthesizing code that satisfies task requirements while remaining consistent with the target repo...
By Kefeng Duan, Dewu Zheng, Yanlin Wang, Terry Yue Zhuo, Mingwei Liu, Jianxing Yu, Jiachi Chen, Ensheng Shi, Xilin Liu, Yuchi Ma, Zibin Zheng
Agent memory faces heterogeneous access needs: a single-hop question may require one piece of evidence, whereas a multi-hop question must combine evidence from multiple sources. Predefined memory work...