Compact0: Learning Generalizable Compaction Intelligence

Long-horizon LLM agents require context management that preserves task-critical information as interaction histories grow. Existing compaction strategies face two generalization challenges: (i) harness generalization, requiring a unified, sufficiently expressive management policy across heterogeneous harnesses; and (ii) domain generalization, requiring effective context management across diverse task types. We introduce Compact0, a dedicated compaction intelligence model for efficient context management across various agent harnesses and task domains. Technically, Compact0 uses a Context Abstraction Layer (CAL) that unifies native context stores through a shared management protocol, enabling harness generalization. For domain generalization, Compact0 learns agentic context organization through CAL to retain, compress, and recover evidence and execution state via supervised fine-tuning followed by reinforcement learning guided by downstream task rewards and compaction quality rubrics. Experiments spanning NL2Repo coding, deep research, streaming QA, and Claw-style benchmarks demonstrate generalization to unseen harnesses and domains, with performance and token-efficiency gains over default harness mechanisms and specialized context-management baselines. These results support compaction intelligence as a transferable and important capability for long-horizon agents.