From the 1 of 4 linked papers with an AI index.
4 papers
Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models
Yubo Wang, Jiarong Liang, Yuxuan Zhang +5
The paper introduces a function-aware fill-in-the-middle (FIM) mid‑training method that masks function calls in code to improve coding agents' ability to incorporate tool outputs,…
VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction
Jiarong Liang, Max Ku, Ka-Hei Hui +2
Evaluating whether Multimodal Large Language Models (MLLMs) genuinely reason about physical dynamics remains challenging. Most existing benchmarks rely on recognition-style protoco…
VisCoder2: Building Multi-Language Visualization Coding Agents
Yuansheng Ni, Songcheng Cai, Xiangchao Chen +8
Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code. However, existing models often fail in practical…
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
Jiarong Liang, Zhiheng Lyu, Zijie Liu +4
Executable software engineering data is valuable for training SWE agents, but scaling it remains difficult for two reasons: only a small fraction of real repository changes yield v…