AttnCompress: Dynamic Attention-Guided Trajectory Compression for Software Engineering Agents
The transition from human-centric assistance to Autonomous Software Engineering (ASE) agents has enabled the resolution of complex real-world SE tasks. However, the trial-and-error nature of these agents generates lengthy interaction trajectories, creating severe bottlenecks in terms of context window limits and cost. While context compression offers a potential remedy, prior approaches suffer from static pruning strategies and granularity mismatches, often failing to preserve the semantic dependencies and syntactic details crucial for SE tasks. To strictly preserve critical task evidence while reducing context length, we introduce AttnCompress, a dynamic attention-guided trajectory compr...
| Authors | Zhengran Zeng, Yixin Li, Rui Xie et al. |
|---|---|
| Primary category | cs.SE |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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