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LoLBench: Evaluating Coding Agents with Long-Horizon Proposals on Large Software Systems

Yun Peng, Zihan Wu, Zeyang Zhuang, Xin Zhou, Rui Shu, Xu Han · Sep 29, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 42% Moderate protocol signal Freshness: Hot Status: Ready
Long Horizon CodingMultilingual
  • Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks with large reference implementations.
  • Across 28 agents we evaluated, the best agent resolves only 14% of tasks and achieves a 52.7% Fail-to-Pass (F2P) pass rate.
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