Tag
FACET is a framework for synthesizing high-quality terminal tasks for AI agent training by preserving source intent and ensuring cross-artifact consistency, leading to improved performance on Terminal-Bench 2.1.
CalibForge is an autonomous terminal-task synthesis system that uses adversarial solver calibration to create learnable tasks for training terminal agents. It constructs 5,431 calibrated tasks and improves agent performance on Terminal-Bench2.0, SWE-bench Pro, and Doc2Repo.
CLI-Universe is a synthesis engine that generates verifiable terminal-agent tasks via multi-dimensional capability taxonomy and evidence-guided research, producing a distilled dataset of 6,000 trajectories. Fine-tuning Qwen3-32B on this dataset achieves 33.4% on Terminal-Bench 2.0, setting a new state-of-the-art for open-source models at or below 32B parameters.
TASTE is an automated method for generating challenging agent benchmarks with broader tool-use coverage by evolving tool sequences through adaptive contrastive n-gram modeling and iterative difficulty refinement. The resulting τ^c-Bench reveals that models nearly saturating existing benchmarks suffer severe performance drops, indicating saturation rather than robust skill.