DMC-5: Dual-Model Collaboration with Cache-aware Context Compression for Long-horizon Agents

Long-horizon agents repeatedly feed an ever-growing interaction trajectory to a specialized agent model. Prefix caching reuses computation over repeated history, but cannot prevent context growth or remove information irrelevant to the current decision. This is especially costly when the specialized agent lacks the cached-input discounts available to general-purpose models, making trajectory compression a natural alternative. However, existing compression methods on selective context risk losing global information, recursively rewriting summary leads to semantic drift, while repeatedly recompressing the full history shifts the growing cost to the compressor. Therefore, we introduce **DMC-5** (Dual-Model Collaboration with CaChe-aware Context Compression), an asymmetric framework that assigns full-history integration to a general compression model and action generation to a specialized agent model. DMC-5 maintains the raw trajectory as an Append-only Canonical View, preserving global access while allowing the compressor to reuse a stable historical prefix instead of recursively rewriting or recomputing prior context. The agent operates only on a compact Decision View with recent raw interactions, reducing repeated input while retaining the information needed for the next action. DMC-5 further incorporates **YAMATO** (integritY-Aware Memory Abstractions for Tool Operations) to preserve execution-critical information during lossy compression. Experiments on AppWorld, OfficeBench, and 8-objective QA show that DMC-5 reduces agent model input by 38.5-57.0% and end-to-end cost by 14.5-28.0% with 78.0-85.0% compressor cache-hit rates, while improving task performance by 1.0-5.8% over the uncompressed baseline.