5 papers
SKT: Skill-Use Training at Scale via Verified Synthetic Data Generation
Zelin Tan, Yiqun Zhang, Hao Li +11
Agent skills have become an important mechanism for equipping language-model agents with reusable procedural knowledge. However, providing skills alone does not guarantee that curr…
SWE-Pruner Pro: The Coder LLM Already Knows What to Prune
Yuhang Wang, Yuling Shi, Shaoqiu Zhang +6
Pruning long context for coding agents has been a vital technology for efficient context management. While existing context pruning methods such as SWE-Pruner realize this by attac…
FastContext: Training Efficient Repository Explorer for Coding Agents
Shaoqiu Zhang, Maoquan Wang, Yuling Shi +12
Large Language Model (LLM) coding agents have achieved strong results on software engineering tasks, yet repository exploration remains a major bottleneck: locating relevant code c…
Self-Harness: Harnesses That Improve Themselves
Hangfan Zhang, Shao Zhang, Kangcong Li +5
The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit d…
SWE-Explore: Benchmarking How Coding Agents Explore Repositories
Shaoqiu Zhang, Yuhang Wang, Jialiang Liang +8
Repository-level coding benchmarks such as SWE-bench have driven a rapid surge in the capabilities of coding agents. Yet they usually treat coding tasks as a holistic, binary predi…