First AutoHarness, then AutoContext, now AutoCompact. I am seeing a rising trend of work that trains models to natively support more of what the harness does. This work specifically trains agents to decide for themselves when to compact. Reminds me of the new paper from Meta that trains models to manage context natively. But how good is this approach? AutoCompact trains the agent to decide when to compact, what working state to keep, and how to resume. A judge first reviews the base…