Tag
Terminal-World introduces a fully automated pipeline that uses agent skills to synthesize high-quality training data for terminal agents, enabling models to outperform baselines with only 1.2% of the training data. The method co-derives task instructions, environments, and teacher trajectories from skill primitives.
TACO is a self-evolving framework that automatically discovers and refines context compression rules for long-horizon terminal agents.
TACO introduces a self-evolving compression framework that automatically learns to shrink redundant terminal interaction history, cutting token overhead ~10% while boosting accuracy 1-4% across TerminalBench and other code-agent benchmarks.