64 upvotes · 20 JUL 2026 · Yuhang Wang, Yuling Shi, Shaoqiu Zhang et al.
This paper proposes a new pruning method for coding agents that prunes tool outputs directly inside the agent, rather than relying on a separate code classifier, and shows it can save up to 39% of tokens while preserving task quality.
5 upvotes · 23 JUL 2026 · Zhongyuan Peng, Dan Huang, Chuyu Zhang et al.
This paper introduces ICAE-Bench, a benchmark for evaluating coding agents that can build software from incomplete product intent, simulating real-world interactive project-building settings. Practitioners in AI and software development may care about this research because it aims to create more realistic and challenging tests for coding agents.