CoACT: Action-Preserving Observation Compression for Coding Agents
LLM-based coding agents solve software-engineering tasks through iterative interactions with development environments, where returned observations accumulate in the context and become a major source of inference cost. Observation compression reduces this cost by shortening observations before they are appended to the context. However, existing methods still exhibit an unsatisfactory efficiency-effectiveness trade-off, as they do not explicitly model how compression affects the agent's subsequent behavior. This paper proposes CoACT, an action-preserving observation compression method for coding agents. CoACT is built on next-action preservation (NAP), which requires a compressed observatio...
| Authors | Haorui Chen, Yuancheng Zhu, Yitong Zhang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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