Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and tool calls, but they do not fully show how agents accumulate evidence through exploration, use it to inform judgments, continually adjust strategies based on feedback, and turn intermediate results into final deliverables. High-quality long-horizon trajectories capture these agentic behavioral patterns and their connections within a single problem-solving process, providing richer experience for sustained autonomous work. However, environment synthesis may complete or simplify the key work underlying these patterns in advance and thus remove the need for exploration, implementation, and verification that the agent was meant to perform. To address this, we introduce TerminalHorizon, a fully automated data synthesis engine for long-horizon terminal agents. Starting from real-world professional work, it uses independent execution to identify key work omitted during construction and uses the resulting evidence to guide environment reconstruction. To extend these patterns across stages, it progressively expands tasks under the same goal, introducing new requirements that build on prior results and call for further exploration and improvement. Using this engine, we construct 1.5K task environments and perform supervised fine-tuning of Qwen3.5-27B on the resulting dataset to obtain TerminalHorizon-27B. We achieve substantial performance gains on all 12 benchmarks spanning coding-agent tasks, agentic tasks in real-world production settings, and general reasoning. Multiple RSI case studies, each with a 12-hour research budget, further demonstrate the model's ability to continue experimenting beyond an initial solution, improve its methods based on feedback, and retain effective improvements in its final deliverables.
Figures & tables
Figure 1: Motivation for TerminalHorizon. Short trajectories can teach agents to perform terminal operations, but not to autonomously complete complex, long-horizon tasks. Our long-horizon trajectories connect agentic behavioral patterns, such as exploration, strategy revision, and verification, into a complete autonomous problem-solving process.
Figure 2: Overview of TerminalHorizon. Challenge-guided synthesis refines task environments through independent execution to preserve core problem-solving work. Progressive horizon expansion deepens tasks along the same goal to support richer agentic behavioral patterns.
Figure 3: Comprehensive analysis of task environments and trajectories in TerminalHorizon.
Figure 4: Interaction turns per trajectory: mean, P50, and P90.
Model
Base Model
Traj.
Scaffold
TB 1.0
TB 2.0
Terminal task synthesis methods
CLI-Universe-8B ( Hua et al., 2026 )
Qwen3-8B
6k
Terminus2
19.1
10.9
TerminalTraj-14B ( Wu et al., 2026b )
Qwen2.5-Coder-14B
50.7k
Terminus2
28.9
19.1
TermiGen-32B ( Zhu et al., 2026 )
Qwen2.5-Coder-32B
3.3k
BashAgent
–
19.3
LiberCoder-32B ( Lin et al., 2026 )
Qwen3-32B
48.3k
OpenHands
38.9
19.5
SkillSynth-14B ( Fan et al., 2026 )
Qwen3-14B
10.7k
Terminus2
22.9
19.9
Table 1: Main results on Terminal-Bench 1.0 and 2.0. Bold marks the best score in each benchmark. - denotes unavailable scores, and † denotes results from official model cards.
Table 2: Performance across coding, agentic, and general reasoning benchmarks.