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AI Agent Papers·2026
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ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents

Chengyi Yang, Renxu Bao +11·Affiliation: Xiamen University, Affiliation: Washington University in St. Louis +2

Converts real repositories into verifiable, sandboxed training environments with black-box tests to train coding agents on long-horizon program reproduction. Provides Program-Env for automated environment construction and Program-Agent for a role-isolated development workflow, yielding 1,648 C/C++/Go/Rust tasks and improving benchmark performance.

#coding-agents#long-horizon#programming#code#benchmark+8
AI Agent Papers·2026
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AgentSWE: Can Coding Agents Build the Agent You Actually Want?

Jiahao Wang, Hongjin Qian +3·Beijing Academy of Artificial Intelligence, Institute of Automation, Chinese Academy of Sciences +1

Measures whether coding agents can build agents that truly satisfy natural-language requirements via AgentSWE: a 25-task benchmark spanning Creation, Editing, and Optimization. Builders develop against few illustrative cases; acceptance runs frozen submissions on held-out cases and requirement-derived rubrics, exposing overfitting, hollow runtimes, and unsupported completion claims.

#coding-agents#ai-agent#benchmark#benchmarks#software-engineering+5
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