1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
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…
SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents
Yuhang Wang, Yuling Shi, Mo Yang +7
LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency…
SWE-QA: Can Language Models Answer Repository-level Code Questions?
Weihan Peng, Yuling Shi, Yuhang Wang +3
Understanding and reasoning about entire software repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and Co…