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Matching papers

SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring

115 upvotes · 10 AUG 2026 · Yuling Shi, Jinghan Xu, Kelin Fu et al.

This paper introduces SWE-Bench ProMax, a new benchmark for testing AI coding agents on large-scale multilingual code refactoring tasks, which is designed to be more realistic and challenging than existing benchmarks. Practitioners can care about this paper because it provides a rigorous evaluation of current AI coding agents' capabilities on a more representative set of tasks.

AI coding agentscode refactoringmultilingual benchmarkssoftware engineeringprogramming languages

CodeMidas: Scaling Agentic Coding RL Environments from Code Itself

82 upvotes · 18 SEP 2026 · Bowen Ye, Lei Li, Shicheng Li et al.

This paper creates a system called CodeMidas that turns existing open-source code into environments for training coding agents using reinforcement learning. Practitioners might care because it could lead to more diverse and effective coding agents that can learn to fix issues, construct code, and verify their own work.

reinforcement learningagentic codingopen-source codebasesprogramming languagestechnical domainstask-specific input