developer tools
The Coding Agent Learned When to Forget
AutoCompact makes context compression a policy decision about timing, retained state and continuation.
Summary
AutoCompact makes context compression a policy decision about timing, retained state and continuation.
Long repository tasks accumulate stale searches and edits long before a context window fills. AutoCompact trains an agent to decide when to compact, what working state to preserve and how to continue, using judged trajectories whose flawed compaction choices and summaries are corrected before execution continues. The preprint reports stronger long-horizon coding behavior than fixed or heuristic compaction baselines; the result is an evaluation of the tested agent and task suite, not evidence that learned summaries are universally lossless.
Why it matters
AutoCompact makes context compression a policy decision about timing, retained state and continuation.
Limits and context
- The preprint reports stronger long-horizon coding behavior than fixed or heuristic compaction baselines; the result is an evaluation of the tested agent and task suite, not evidence that learned summaries are universally lossless.
Key claims
AutoCompact makes context compression a policy decision about timing, retained state and continuation.
Qualification: The preprint reports stronger long-horizon coding behavior than fixed or heuristic compaction baselines; the result is an evaluation of the tested agent and task suite, not evidence that learned summaries are universally lossless.
Evidence: source-2026-10-03-004
Sources
- arXiv preprint 2610.02163arXiv · primary research
Corrections
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