Lost in Compaction

Context compaction is now standard in long-running agent systems, and it silently drops the instructions users most expect to persist. This work names that class, Session Constraints, instructions like "do not delete any emails until I confirm" meant to bind behavior for the rest of a session, and introduces COMPINT to evaluate compactors across multi-turn chat, agentic trajectory, and long-horizon research. Current compactors retain only 17% of injected constraints on average, and most leave the task worse off than running it without compaction at all. Retention swings with the compactor, the prompt, the context length, the phrasing, and where the constraint was injected, which is what makes the loss structural rather than a quirk of one setup. The fix is small and does not touch the compactor or the model: an SC-aware extractor running alongside as a plug-and-play module recovers over 90% retention in all three scenarios.