Summary (Overview)

• Terminal-Universe is a novel framework that reconstructs executable terminal environments from recorded agent trajectories, inverting the traditional environment→trajectory mapping.
• The framework uses a two-stage reconstruction process: deterministic replay of file operations followed by agentic completion to restore missing context, yielding 37.3k task-sufficient environments.
• Four complementary re-querying mechanisms scale task utility: Intent Recovery (reconstructing original tasks), Single-WS (within-workspace synthesis), Cross-WS (cross-workspace breadth expansion), and Multi-Round (multi-turn depth expansion).
• Fine-tuning Qwen3.5-27B on the resulting corpus improves Terminal-Bench 2.1 by 11.9 points (46.2→58.1) and EvoCode-Bench v2 MT@4 by 13.8 points (6.3→20.1).
• Ablations confirm that re-solving recovered tasks in reconstructed environments far outperforms imitating raw trajectories (52.1 vs. 36.7 on Terminal-Bench 2.1).

Introduction and Theoretical Foundation

The paper addresses a critical gap in terminal-based code agent training: while agent trajectories have accumulated at scale, realistic, executable environments remain scarce. The key insight is that trajectories and environments are two views of the same episode—a trajectory is a single frozen demonstration, while an environment can be re-queried into many verifiable tasks with execution feedback.

Why environments matter more than trajectories:

• A trajectory's quality is bounded by the policy model that produced it
• Trajectories cannot be verified for correctness
• Environments allow re-solving by stronger models, verification via custom tests, and posing harder tasks

Existing approaches and their limitations:

1. Repository-based methods (SWE-Gym, R2E-Gym): Roll back git history to create environments—limited to repair tasks from past bugs
2. Perturbation methods (SWE-smith, CLI-Gym): Inject bugs into working repositories—task range limited by injectable bug types
3. Task-conditioned synthesis (Endless Terminal, CLI-Universe): Generate tasks and environments from scratch—environments lack realism of real projects

The novel insight: Tool calls recorded inside trajectories reveal the content and structure of the environments they ran in. The Read tool shows file contents; Write and Edit show how the workspace changed. This is sufficient to rebuild an executable copy of the original workspace.

Methodology

3.1 Environment Reconstruction

The reconstruction proceeds in three stages:

Stage 1: Deterministic replay. Processes read/write/edit operations chronologically to recover, for each accessed path, the earliest and latest file contents visible in the trajectory. The reconstructed initial workspace $\widehat{E}_0$ collects each pre-existing file at its earliest observed version before the agent's first change; files created by the agent are excluded.

Stage 2: Agentic completion. Given the partial workspace $\widehat{E}_0$ and recovered task $q$, a completion agent creates missing files, completes partial files, and restores dependencies needed to make $q$ solvable without implementing it.

Stage 3: Environment filtering. An agentic judge inspects each workspace with read-only shell/file tools and labels it sufficient or insufficient based on whether its source, configuration, data, and structure give a capable agent enough context.

3.2 Re-querying Mechanisms

Intent Recovery: Normalizes trajectories into chronological streams, consolidating user requests into self-contained tasks.

Single-WS (Single-Workspace Synthesis): An offline generator synthesizes five candidate tasks per workspace under groundedness, structural diversity, and verifiability constraints.

Cross-WS (Breadth Expansion): Discovers directional dependency relationships across environments via TF-IDF nearest-neighbor search and LLM judgment. Each task pairs a writable target workspace with a read-only reference workspace mounted at a separate path.

Multi-Round (Depth Expansion): Extends initial queries into iterative sessions with:

1. Evolving task specification via a requirement tracker
2. Round-level verification and feedback with three interaction styles: feature extension, feature revision, and feature conflict

3.3 Verification and Filtering

• Each task is paired with an agent-authored pytest suite verified through iterative local execution
• Solutions are rolled out using Qwen3.7-Max as teacher (temperature 1.0, top-p = 0.95, 256k context)
• Trajectories accepted only if all tests pass; Multi-Round uses round-level selection

Empirical Validation / Results

Reconstruction Statistics

Main Results (Table 3)

Key Ablations

Task Re-solving (Table 4): Intent Recovery (52.1) substantially outperforms source-trajectory SFT (36.7), confirming reconstruction is essential.

Agentic Completion (Table 5): Replay + completion (52.9) beats replay-only (48.7), with lower variance (±1.4 vs. ±3.5).

Verifier Filtering (Table 6): Matters more for harder Cross-WS tasks (55.4 with filtering vs. 53.2 without) than Single-WS (56.4 vs. 56.0).

Breadth Expansion (Table 7): Single-WS + Cross-WS (58.4) exceeds Single-WS alone (56.4). Cross-WS trajectories are 1.6× longer in turns, 1.9× more tool calls, with teacher pass@1 dropping from 72.3% to 49.2%.

Depth Expansion (Table 9): Adding Multi-Round raises MT@4 from 18.4 to 21.0; round-level verification contributes +2.2 MT@4 and +3.7 Case score.

Data Scaling (Table 10): Under matched budgets, environment expansion (+2.8 points) beats query expansion (+0.6) and solution expansion (+0.7).

Cross-domain Transfer (Table 11): SWE trajectories improve terminal performance from 47.0 to 50.0, showing the pipeline transfers across domains.

Theoretical and Practical Implications

For environment scaling: Terminal-Universe demonstrates that recorded trajectories are a viable, scalable source of executable environments—a previously underexplored resource. The framework inverts the conventional direction: instead of generating environments from scratch or perturbing existing ones, it recovers them from observational data.

For task synthesis: The breadth (cross-workspace) and depth (multi-round) expansions reproduce routine patterns of real engineering practice. Cross-workspace tasks require agents to reconcile information across codebases, while multi-round sessions capture iterative user feedback—both are closer to real-world development than single-turn, single-repository tasks.

For training data quality: The comparison between source-trajectory SFT (36.7) and Intent Recovery (52.1) provides strong evidence that regenerating demonstrations under a consistent teacher policy provides more effective supervision than imitating heterogeneous source trajectories.

For data budgeting: The matched-budget ablation (Table 10) shows that collecting more environments is more valuable than generating more queries or solutions per environment, supporting the paper's core thesis.

Practical considerations: The use of standard Ubuntu 24.04 containers (rather than repository-specific images) lowers cost and simplifies deployment, though it may reduce fidelity for edge cases requiring specialized system dependencies.

Conclusion

Terminal-Universe reframes recorded agent trajectories from fixed demonstrations into recoverable, reusable execution environments. Through deterministic replay and agentic completion, it restores latent workspace state; through four re-querying mechanisms, it scales task utility across breadth (cross-workspace) and depth (multi-round) dimensions. Empirical validation shows:

• Re-solving recovered tasks outperforms SFT on source trajectories by 15.4 points
• Verifier-selected trajectories improve Terminal-Bench 2.1 by 11.9 points and EvoCode-Bench v2 MT@4 by 13.8 points
• The pipeline scales naturally with richer source trajectories and transfers across domains

Future directions include: building tailored repository-specific environments, expanding beyond source trajectory coverage distributions, and using multiple teachers with independent verifier construction to mitigate single-teacher capability gaps.