TACO explores context compression for terminal agents
A framework discovers and refines rules for compressing context from terminal-agent interactions. The paper reports tests on three benchmarks.
Published on April 22, 2026, the paper presents TACO, a framework that discovers and refines context-compression rules from terminal-agent interaction trajectories. The approach aims to reduce noisy observations in the context of long-running tasks.
The work reports tests on TerminalBench, SWE-Bench Lite, and CompileBench, but the available material gives no quantitative results. Engineers building terminal agents can consult the paper and verify its methods, configurations, and results for each benchmark. If using AI to study or apply the approach, avoid sending confidential code or personal data without first reviewing the organization’s privacy policy and controls.