SelfCompact

SelfCompact: Model-driven adaptive context compaction for long agent traces

Researchers propose SelfCompact, a scaffold that lets language models decide when and how to compact their own accumulated context during long agentic runs, rather than relying on fixed token-threshold triggers. The system pairs a compaction tool with a lightweight rubric specifying when to invoke or suppress compaction based on trajectory structure (e.g., sub-task completion vs. mid-derivation). Evaluated across six benchmarks and seven models, SelfCompact matches or exceeds fixed-interval summarization while reducing per-question token cost by 30-70%, with gains of up to 18.1 points on math tasks and 5-9 points on agentic search. The work identifies a 'meta-cognitive gap' in unprompted models and shows it can be closed via scaffolding without fine-tuning.