Action-Preserving Observation Compression

A team from Tsinghua University's Institute for AI proposes CoACT, an observation compression method for coding agents that optimizes for preserving the agent's next action rather than textual similarity to the original context, avoiding removal of information needed later. Across 200 SWE-bench Verified instances and three agent models, CoACT cuts total token consumption by 33.0% on average while PASS@1 moves from 69.0% to 71.0%, keeping task performance essentially intact. The work was accepted directly to ICSE 2027, a CCF-A conference.