most citedDevstral: Fine-tuning Language Models for Coding Agent Applications

2 citations · 5 across the 15 of their papers we have counts for

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cs.SE2026

Enhancing Software Engineering Through Closed-Loop Memory Optimization

Xuehang Guo, Zora Zhiruo Wang, Qingyun Wang +3

Large language models (LLMs) have enabled powerful software engineering (SE) agents capable of navigating complex codebases and resolving real-world issues. However, these agents r…

cs.SE2026

SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution

Gangda Deng, Zhaoling Chen, Zhongming Yu +11

Real-world software must continuously evolve to meet ever-changing and open-ended requirements. AI agents, increasingly deployed as long-running systems, are now entrusted to drive…

cs.SE2025

The OpenHands Software Agent SDK: A Composable and Extensible Foundation for Production Agents

Xingyao Wang, Simon Rosenberg, Juan Michelini +8

Agents are now used widely in the process of software development, but building production-ready software engineering agents is a complex task. Deploying software agents effectivel…

cs.SE2025

TOM-SWE: User Mental Modeling For Software Engineering Agents

Xuhui Zhou, Valerie Chen, Zora Zhiruo Wang +3

Recent advances in coding agents have made them capable of planning, editing, running, and testing complex code bases. Despite their growing ability in coding tasks, these systems…

cs.SE2025★ 2 cited

Devstral: Fine-tuning Language Models for Coding Agent Applications

Abhinav Rastogi, Adam Yang, Albert Q. Jiang +100

We introduce Devstral-Small, a lightweight open source model for code agents with the best performance among models below 100B size. In this technical report, we give an overview o…