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A new paper introduces COMPINT, an evaluation suite showing that context compactors silently drop session constraints, retaining only 17% on average. A simple SC-aware extractor recovers over 90% retention without modifying the compactor or model.
This paper introduces CompInt, an evaluation suite for measuring how well context compaction preserves user-issued session constraints in LLM systems. It finds current compactors retain only 17% of constraints on average and proposes an SC-aware extractor that achieves over 90% retention without modifying the compactor or LLM.