mlsanityData to 13 Oct 2026

Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments

arXiv 2609.04148 ↗First posted 3 Sep 2026cs.AI cs.CLcited by 4

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Abstract (arXiv)

As terminal-based code agents become prevalent, agent trajectories have accumulated at scale, while realistic, executable environments remain scarce. However, environments are what agent post-training actually requires: each can be re-queried into many verifiable tasks and provides execution feedback, whereas a trajectory is a single frozen demonstration. Rather than generating environments from scratch, we observe that the tool-execution history in existing trajectories exposes the structure and contents of the environments in which they ran, making it possible to reconstruct those environments from the trajectories themselves. Thus, we introduce Terminal-Universe, a framework which turns each trajectory into a reusable environment and explores it for synthesizing new tasks and continued interactions. Specifically, Terminal-Universe replays the file operations recorded in a trajectory to restore each file before the agent modified it, yielding a partial workspace; a completion agent then supplies the missing files and dependencies. On this recovered workspace, we both reconstruct the original intent task and synthesize entirely new ones. Besides, we also scale the tasks along two complementary axes: breadth and depth. For breadth, we mine directional dependency relations between related environments and synthesize cross-workspace queries spanning multiple codebases, as developers routinely do in real-world development. For depth, we extend the initial single-turn query into a multi-round session that captures iterative user feedback and requirement refinement via a user agent. Applied to public terminal agent trajectories, Terminal-Universe produces 37.3k task-sufficient environments. Supervised fine-tuning of Qwen3.5-27B on this corpus improves single-round performance on Terminal-Bench 2.1 by 11.9 points and multi-round performance on EvoCode-Bench v2 MT@4 by 13.8 points.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 3 Sep 2026.

Builds on

26 of 35 references.

References · 26

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,274
  2. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 547
  3. 2024Training Software Engineering Agents and Verifiers with SWE-Gymcited by 315
  4. 2025SWE-smith: Scaling Data for Software Engineering Agentscited by 263
  5. 2023InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedbackcited by 234
  6. 2025R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agentscited by 143
  7. 2025SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agentscited by 131
  8. 2026Endless Terminals: Scaling RL Environments for Terminal Agentscited by 47
  9. 2026On Data Engineering for Scaling LLM Terminal Capabilitiescited by 39
  10. 2026Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environmentscited by 29
  11. 2026Tmax: A simple recipe for terminal agentscited by 27
  12. 2026TermiGen: High-Fidelity Environment and Robust Trajectory Synthesis for Terminal Agentscited by 25
14 more
  1. 2026CLI-Gym: Scalable CLI Task Generation via Agentic Environment Inversioncited by 20
  2. 2026Toward Scalable Terminal Task Synthesis via Skill Graphscited by 18
  3. 2026Terminal-World: Scaling Terminal-Agent Environments via Agent Skillscited by 16
  4. 2026SWE-Together: Evaluating Coding Agents in Interactive User Sessionscited by 14
  5. 2026Recursive Synthesis for Long-Horizon Terminal Taskscited by 10
  6. 2026LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agentscited by 8
  7. 2026Dockerless: Environment-Free Program Verifier for Coding Agentscited by 8
  8. 2026SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessionscited by 8
  9. 2026EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactionscited by 6
  10. 2026What Makes Interaction Trajectories Effective for Training Terminal Agents?cited by 6
  11. 2026ICAE-Bench: Evaluating Coding Agents as Interactive Project Builderscited by 5
  12. 2026CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Taskscited by 5
  13. 2026FACET: Preserving Source Intent and Executable State in Terminal Task Synthesiscited by 4
  14. 2026Meta-Task: Turning Terminal Task Synthesis into a Terminal Task for Scalable Agent Trainingcited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 4.

4 papers.