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arxiv: 2605.22535 · v1 · pith:MXWJYBYRnew · submitted 2026-05-21 · 💻 cs.AI

TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks

Zhaoyang Chu , Jiarui Hu , Xingyu Jiang , Pengyu Zou , Han Li , Chao Peng , Peter O'Hearn , Earl T. Barr
show 3 more authors
Mark Harman Federica Sarro He Ye
This is my paper

Pith reviewed 2026-05-22 05:49 UTC · model grok-4.3

classification 💻 cs.AI
keywords terminal agentsAI benchmarkingcommand-line tasksagent evaluationreverse engineeringreal-world workflowsdeveloper tools
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The pith

A benchmark built from 80,870 real terminal recordings shows frontier agents reach only 62.5 percent success on authentic developer workflows.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents TerminalWorld as a scalable engine that automatically extracts evaluation tasks from thousands of in-the-wild terminal recordings. It produces a benchmark of 1,530 tasks across 18 categories and a manually verified subset of 200 tasks. Testing eight frontier models and six agents on the verified tasks finds a top pass rate of 62.5 percent. Scores on this benchmark show only weak correlation with those on existing expert-curated suites. The engine keeps the tasks current by construction as real developer practices change.

Core claim

TerminalWorld is generated by an automated reverse-engineering engine that processes 80,870 terminal recordings into 1,530 validated tasks covering 1,280 unique commands and workflows up to 50 steps long. On a curated Verified set of 200 tasks, current systems achieve at most a 62.5 percent pass rate. The resulting scores correlate only weakly (Pearson r = 0.20) with performance on prior expert-curated benchmarks such as Terminal-Bench, indicating that TerminalWorld measures distinct real-world terminal capabilities.

What carries the argument

The automated reverse-engineering engine that converts raw terminal recordings into high-fidelity, validated tasks spanning everyday to multi-step developer workflows.

If this is right

  • Agents that succeed on expert-curated terminal benchmarks may still fail on tasks drawn directly from real usage.
  • The benchmark can be refreshed automatically whenever new terminal recordings become available.
  • Long workflows exceeding 50 steps remain especially difficult for current agent designs.
  • Evaluation of terminal agents should incorporate metrics that track fidelity to actual command sequences rather than isolated subtasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Progress measured on static expert benchmarks may overestimate readiness for live terminal use.
  • The same engine could be applied to other environments such as shell scripting in containers or remote servers.
  • Teams could adopt the verified subset as a lightweight test for agent deployment in production tooling.

Load-bearing premise

The reverse-engineering process yields tasks that faithfully reflect genuine developer practices without introducing systematic biases in selection or validation.

What would settle it

Human developers performing the same tasks achieve substantially lower success rates than the reported agent ceiling, or agents that score high on TerminalWorld fail at comparable rates when placed in live developer environments.

Figures

Figures reproduced from arXiv: 2605.22535 by Chao Peng, Earl T. Barr, Federica Sarro, Han Li, He Ye, Jiarui Hu, Mark Harman, Pengyu Zou, Peter O'Hearn, Xingyu Jiang, Zhaoyang Chu.

Figure 1
Figure 1. Figure 1: An overview of the TERMINALWORLD pipeline. Our data engine automates terminal task synthesis through four key stages: (1) Collecting Human Recordings harvests in-the-wild developer operations; (2) Synthesizing Terminal Tasks distills an outcome-oriented task instruction and a clean reference solution; (3) Reproducing Executable Environments creates and refines an isolated Docker container to replay the cor… view at source ↗
Figure 2
Figure 2. Figure 2: Statistical comparison of 1,530 TERMINALWORLD tasks and 241 unique Terminal￾Bench tasks. (a) highlights that TERMINALWORLD captures diverse real-world workflows (e.g., container orchestration, CI/CD) severely underrepresented in expert-curated benchmarks. (b) shows a natural spectrum that mirrors everyday terminal usage, from short operations to multi-step workflows. (c) reveals an extensive vocabulary of … view at source ↗
Figure 3
Figure 3. Figure 3: Performance of frontier LLMs across terminal task categories. The results indicate that LLMs still struggle with complex tasks involving performance optimization, scripting & automation, and debugging & testing, showing domain-specific blind spots and a lack of general tool-use ability. We characterize the benchmark’s representativeness, diversity, and complexity by comparing it against Terminal-Bench [Mer… view at source ↗
Figure 4
Figure 4. Figure 4: Scatter of Terminal-Bench 2.0 score vs. TERMINALWORLD-VERIFIED score. The weak score correlation (Pearson r = 0.20) highlights a disconnect: LLMs that dominate expert-curated challenges still struggle on real-world workflows, indicating that TERMINALWORLD assesses a distinct terminal capability. Agent Frameworks Drive Cost-Effectiveness More Than Capabilities. The results suggest that agent frameworks affe… view at source ↗
Figure 5
Figure 5. Figure 5: Command-Set Overlap of Agents with Human Workflows. While tasks are derived from real-world human record￾ings, agents often reach the correct outcome through different command paths, with 21.4% median command-set overlap. More detailed analysis is provided in Appendix C.4. Agents Solve Tasks Through Alternative Com￾mand Paths, Not Mimicking Humans. The me￾dian overlap is only 21.4%, meaning agents typi￾cal… view at source ↗
Figure 6
Figure 6. Figure 6: Agents vs. Humans. Tasks re￾quiring many reference commands are consis￾tently harder for agents, even when humans complete them quickly. Reference command count is the clearer predictor: tasks requiring 21+ commands remain consistently hard across all time bins (25.0%–41.2%), whereas tasks with 6–10 commands achieve up to 70.6%. Hu￾man completion time, by contrast, is a noisier signal because it conflates … view at source ↗
Figure 7
Figure 7. Figure 7: Agent Command Count vs. Reference Command Count across Eight Models. Refer￾ence command count does not tightly predict how many commands an agent will issue. Failed tasks consistently require far more commands than successful ones, reflecting unproductive exploration when the agent cannot identify the correct solution path. command count with the reference solution command count across all eight evaluated … view at source ↗
read the original abstract

We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from "in-the-wild" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 18 real-world categories, ranging from short everyday operations to workflows exceeding 50 steps, and covering 1,280 unique commands. From these, we curate a Verified subset of 200 representative, manually reviewed tasks. Comprehensive benchmarking on TerminalWorld-Verified across eight frontier models and six agents reveals that current systems still struggle with authentic terminal workflows, achieving a maximum pass rate of only 62.5%. Moreover, TerminalWorld captures real-world terminal capabilities distinct from existing expert-curated benchmarks (e.g., Terminal-Bench), with only a weak correlation to their scores (Pearson r=0.20). The automated engine makes TerminalWorld authentic and scalable by construction, enabling it to evaluate agents in real-world terminal environments as developer practices evolve. Data and code are available at https://github.com/EuniAI/TerminalWorld.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces TerminalWorld, a scalable benchmark generated by automatically reverse-engineering 1,530 validated tasks (plus a 200-task manually reviewed Verified subset) from 80,870 in-the-wild terminal recordings. The tasks span 18 categories and 1,280 unique commands. Benchmarking eight frontier models and six agents on the Verified subset yields a maximum pass rate of 62.5%. Scores on TerminalWorld correlate only weakly (Pearson r=0.20) with those on Terminal-Bench, which the authors interpret as evidence that TerminalWorld captures distinct real-world terminal capabilities. The automated engine is presented as enabling ongoing, authentic evaluation as developer practices evolve.

Significance. If the reverse-engineered tasks faithfully reflect real developer workflows, TerminalWorld would offer a valuable, scalable alternative to expert-curated benchmarks for evaluating terminal agents. The open release of data and code at the provided GitHub repository is a clear strength that supports reproducibility and community extensions. The empirical demonstration of current limitations (62.5% ceiling) and the low correlation with existing benchmarks could help guide future agent development toward more authentic terminal workflows.

major comments (2)
  1. [§3] §3 (Reverse-Engineering Engine): The central claim that TerminalWorld is 'authentic and scalable by construction' and captures capabilities 'distinct from existing expert-curated benchmarks' rests on the assumption that the automated extraction from 80,870 recordings preserves developer intent, error-recovery steps, and session context. The manuscript provides only a high-level description of the engine and reports manual review of a 200-task subset; no quantitative fidelity metrics (e.g., inter-annotator agreement on intent preservation or comparison of task distributions before/after filtering) are given for the full 1,530 tasks. This is load-bearing for the distinctness interpretation of r=0.20.
  2. [Results] Results section (correlation analysis): The reported Pearson r=0.20 between TerminalWorld and Terminal-Bench is used to argue that the benchmark measures new capability dimensions. However, the manuscript does not specify which exact model/agent scores were used to compute the correlation, whether the correlation is computed on the full set or Verified subset, or whether confidence intervals or p-values accompany the coefficient. Without these details the strength of the 'distinct' claim cannot be fully assessed.
minor comments (2)
  1. [Abstract / Results] The abstract states 'eight frontier models and six agents' but the results tables and text do not clearly tabulate per-model and per-agent pass rates with error bars or sample sizes, making it difficult to interpret the 62.5% maximum.
  2. [§4] The description of task categories and length distribution (short operations to >50-step workflows) would benefit from a supplementary table or figure showing the breakdown by category and step count for both the full and Verified sets.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments on our work. We provide detailed responses to the major comments below, indicating where revisions will be made to address the concerns.

read point-by-point responses

  1. Referee: §3 (Reverse-Engineering Engine): The central claim that TerminalWorld is 'authentic and scalable by construction' and captures capabilities 'distinct from existing expert-curated benchmarks' rests on the assumption that the automated extraction from 80,870 recordings preserves developer intent, error-recovery steps, and session context. The manuscript provides only a high-level description of the engine and reports manual review of a 200-task subset; no quantitative fidelity metrics (e.g., inter-annotator agreement on intent preservation or comparison of task distributions before/after filtering) are given for the full 1,530 tasks. This is load-bearing for the distinctness interpretation of r=0.20.

    Authors: We acknowledge the importance of demonstrating fidelity for the full set of tasks. The reverse-engineering engine incorporates several automated checks for task validity, including verification that commands execute successfully and that the task description accurately reflects the recorded actions. For the Verified subset, manual review confirmed high fidelity in intent preservation. We did not perform inter-annotator agreement as the review was conducted by domain experts following a standardized protocol. In the revised version, we will expand the description of the engine with more specifics on the validation steps and provide a comparison of command distributions and task lengths before and after filtering to support the scalability and authenticity claims. revision: partial

  2. Referee: Results section (correlation analysis): The reported Pearson r=0.20 between TerminalWorld and Terminal-Bench is used to argue that the benchmark measures new capability dimensions. However, the manuscript does not specify which exact model/agent scores were used to compute the correlation, whether the correlation is computed on the full set or Verified subset, or whether confidence intervals or p-values accompany the coefficient. Without these details the strength of the 'distinct' claim cannot be fully assessed.

    Authors: The Pearson correlation of 0.20 was calculated using the performance scores of the eight frontier models and six agents evaluated on the TerminalWorld-Verified subset, compared against their reported scores on Terminal-Bench. We will revise the Results section to explicitly detail the models and agents included in this analysis, confirm that it uses the Verified subset, and include the p-value along with 95% confidence intervals for the correlation coefficient. This will allow readers to better evaluate the evidence for distinct capabilities. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark construction and measurements are self-contained

full rationale

The paper introduces TerminalWorld via an automated reverse-engineering engine applied to 80,870 terminal recordings, yielding 1,530 tasks and a 200-task Verified subset. Central results are direct empirical measurements: maximum agent pass rate of 62.5% on the Verified set and Pearson r=0.20 correlation with Terminal-Bench. These are experimental outcomes on newly collected data, not derivations, fitted parameters renamed as predictions, or results forced by self-citation chains. The phrase 'authentic and scalable by construction' describes the methodological pipeline for data collection and task extraction rather than a self-referential loop where an output is defined in terms of itself. No equations or uniqueness theorems are invoked that reduce the claims to prior inputs. The work is self-contained against external benchmarks and does not rely on load-bearing self-citations for its core claims.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on the assumption that terminal recordings can be automatically turned into faithful tasks and that manual review of 200 tasks is sufficient to certify the larger set. No free parameters are explicitly fitted to performance data; the main choices are in the engine's filtering rules and category definitions.

axioms (1)
  • domain assumption Terminal recordings from real users contain representative developer workflows that can be reverse-engineered into evaluation tasks without major distortion.
    Invoked in the description of the data engine processing 80,870 recordings to yield validated tasks.

pith-pipeline@v0.9.0 · 5750 in / 1318 out tokens · 28870 ms · 2026-05-22T05:49:48.570662+00:00 · methodology

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Reference graph

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