Self-Compacting Language Model Agents

Researchers from Johns Hopkins University introduced SELFCOMPACT, a new scaffold that lets language model agents decide when and how to compact their context during long reasoning or tool use. This adaptive compaction method improves performance on math and agentic search benchmarks while reducing token costs compared to fixed-interval summarization.