Source-linked AI summary
LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces
Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Yu Qiao, Shenglin Zhang, Kaipeng Zhang
TL;DR
Existing benchmarks inadequately test the long-horizon, sequential work required for realistic software engineering because they are often short, contamination-prone, and coarse-grained. LongCLI-Bench addresses this with curated CLI tasks, dual-set testing, and step-level scoring, finding that current agents achieve low pass rates while human-agent collaboration substantially improves performance. The benchmark remains limited by costly task creation and metrics that do not fully assess code quality or efficiency.
Problem
Existing benchmarks are limited by short horizons, GitHub-derived contamination, and insufficiently fine-grained evaluation of sequential software-engineering tasks.
Method
LongCLI-Bench curates 20 long-horizon CLI tasks from over 1,000 assignments and workflows, using dual-set tests for requirements and regressions plus step-level scores.
Results
Current agent systems achieve pass rates below 20% on LongCLI-Bench, while human-agent collaboration substantially improves performance.
Takeaways & Limitations
Planning, execution proficiency, and synergistic human-agent workflows remain central priorities for improving long-horizon agentic programming.
Takeaways & Limitations
Task creation averages 40 hours per task, and step-level scores do not fully assess code quality or efficiency.
Abstract
from arXiv · showhide
Recent advances in AI-assisted programming have empowered agents to execute complex workflows via command-line interfaces, however, existing benchmarks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics, fail to rigorously evaluate the long-horizon planning and execution capabilities essential for realistic software engineering. To address these gaps, we introduce LongCLI-Bench, a comprehensive benchmark designed to evaluate agentic capabilities across long-horizon, realistic tasks. We curated 20 high-quality, long-horizon tasks from over 1,000 computer science assignments and real-world workflows, covering four engineering categories: from scratch, feature addition, bug fixing, and refactoring. We propose a dual-set testing protocol for LongCLI-Bench, which measures requirement fulfillment (fail-to-pass) and regression avoidance (pass-to-pass), and incorporates step-level scoring to pinpoint execution failures. Extensive experiments reveal that even state-of-the-art agents achieve pass rates below 20% in LongCLI-Bench. Step-level analysis further indicates that the majority of tasks stall at less than 30% completion, highlighting that critical failures often occur in the early stages. Although self-correction offers marginal gains, human-agent collaboration through plan injection and interactive guidance yields significantly higher improvements. These results highlight that future research must emphasize the development of synergistic human-agent workflows alongside advances in agents' planning and execution capabilities to overcome key challenges in long-horizon task performance.
1 Introduction
Existing CLI agents motivate benchmarks that measure realistic, long-horizon software engineering rather than isolated or short tasks. LongCLI-Bench addresses this gap with diverse tasks, dual-set evaluation, and step-level diagnostics, while experiments expose persistent planning and execution failures.
- Motivation: Existing repository-level benchmarks remain short, narrow, contamination-prone, or insufficiently fine-grained for diagnosing agent failures.These limitations leave sequential dependencies and long-term environmental context under-evaluated.
- Benchmark proposal: LongCLI-Bench evaluates long-horizon agentic capabilities in realistic CLI environments across diverse engineering categories with fine-grained evaluation.The benchmark targets realistic workflows rather than a single task type.
- Benchmark proposal: LongCLI-Bench curates tasks from 958 CS assignments and 50 real-world workflows spanning from-scratch development, feature addition, bug fixing, and refactoring.The sources are intended to reduce contamination risk associated with GitHub-derived datasets.
- Evaluation: Fail→Pass tests assess new requirements, Pass→Pass tests assess regression avoidance, and step-level scores measure partial progress and failure locations.Together, these mechanisms distinguish requirement implementation from preservation of existing functionality.
- Results: All agent systems achieve < 20% pass rate, with failures predominantly concentrated in early task stages.Self-correction provides limited improvement, whereas human plan injection and interaction perform better.
- Implications: Future research should emphasize planning, execution proficiency, long-horizon context consistency, and effective human collaboration.The paper presents synergistic human-agent workflows as a priority alongside stronger autonomous capabilities.
2 Related Work
Code-generation evaluation has progressed from local correctness toward repository- and environment-grounded tasks. Existing benchmark families increasingly cover repositories, features, long horizons, generation, testing, and interactive environments.
- Agents and scaffolds: Agentic coding performance depends jointly on language-model capabilities and the agent scaffold.Examples include code-oriented models, repository-interacting agents, and end-to-end frameworks.
- Benchmark evolution: Early benchmarks assess function- or class-level correctness but largely overlook cross-file planning.Later suites broaden domains and introduce explicit cross-file reasoning and freshness-oriented evaluation.
- Repository-level evaluation: SWE-bench evaluates repository-level GitHub issue resolution through patches that satisfy project tests.Other benchmarks extend evaluation to feature and long-horizon tasks, repository generation, and test generation.
3 LongCLI-Bench
LongCLI-Bench is organized around five design principles: long-horizon tasks, contamination control, clear requirements, solvability, and isolated environments.
- LongCLI-Bench is designed around five principles: long-horizon, contamination control, clear requirements, solvability, and isolated environments.
3.1 Data Construction
LongCLI-Bench combines manually curated assignments and realistic workflows with independently constructed environments, solutions, and tests. Its verification process checks both requirement fulfillment and preservation of existing functionality.
- Data sources: Tasks are curated from CS course assignments and manually constructed real-world research and engineering workflows.The sources target realistic codebases, clear specifications, and complex sequences of dependent subtasks.
- Requirement design: Requirements explicitly specify functional goals and entry points to reduce false negatives caused by arbitrary or unlocatable interfaces.
- Requirement design: Assignment requirements are partially rewritten, while real-world requirements are entirely manually crafted, reducing simple retrieval matching and contamination risks.
- Environment construction: Execution environments and human solution codebases are constructed concurrently in Docker, with dependencies separated into a Dockerfile and solutions retained for solvability verification.
- Test construction: Tests are written from requirements rather than the solution repository, preventing implementation details from being baked into evaluation.
- Verification: Fail→Pass tests verify requirements, whereas Pass→Pass tests verify that modifications preserve existing system functionality.Both test sets contribute to step-level scores that quantify partial progress.
- Verification: A valid task requires initial F2P failure and solution-repository F2P success, with P2P tests passing on both repositories.
- Verification: Iterative closed-loop verification reviews documentation, executes tests on both repositories, and repairs issues when the verification condition fails.
3.2 Task Composition and Evaluation
LongCLI-Bench evaluates agents in isolated environments through requirement-driven execution, testing, and step-level scoring. Its protocol supports both repeated attempts and multi-turn self-correction.
- Task Composition: Each task includes an initial repository, explicit requirements, an isolated environment, and metadata describing task characteristics.The benchmark also includes a scoring parser for analyzing test outputs.
- Evaluation: The evaluation initializes a Docker environment, lets the agent execute through the terminal, runs tests, and aggregates results into pass rates and step scores.
- Evaluation: Optional protocols evaluate performance across multiple independent attempts or self-correction using feedback from previous turns.
3.3 Taxonomy
LongCLI-Bench classifies tasks across four software-engineering activities and six technical domains, covering capabilities from project creation through maintenance and restructuring.
- Engineering Categories: The taxonomy includes From Scratch, Feature Addition, Bug Fix, and Refactor tasks.These represent building projects, adding modules, diagnosing bugs, and restructuring code, respectively.
- Technical Domains: The benchmark spans six domains: System Programming, Web Development, Data Engineering, Machine Learning, Applications, and DevOps.
- Technical Domains: System Programming covers operating systems, compilers, memory, concurrency, embedded programming, hardware interfacing, and distributed systems.
- Technical Domains: Web Development includes frontend and backend systems, databases, API design, authentication, and services.
- Technical Domains: Data Engineering, Machine Learning, Applications, and DevOps cover data processing, model lifecycles, interactive applications, and operational infrastructure.
3.4 Statistics of LongCLI-Bench
LongCLI-Bench contains substantially larger and more time-intensive tasks than Terminal-Bench@2, targeting repository-level engineering rather than isolated snippets.
- Task Scale: LongCLI-Bench averages 15,000+ LoC and 104 source files per task, compared with 227.7 LoC and 0.69 files for Terminal-Bench@2.The tasks span C, Python, Java, and JavaScript.
- Task Scale: The benchmark’s scale and completion time are intended to probe long-horizon planning and context maintenance in complex, interdependent systems.
- Task Scale: Expert completion averages 1000+ minutes for LongCLI-Bench versus 206.7 minutes for Terminal-Bench@2.The time difference reflects the benchmark’s more demanding temporal dimension.
4 Experiments
Experiments show that current agents struggle with long-horizon CLI tasks, especially early planning and regression avoidance. Self-correction helps modestly, whereas human guidance and combined planning produce stronger improvements.
- 4.2 Main Results: Most models achieve pass rates below 15% in single-turn evaluation, with Claude-Opus-4.6 reaching 16.7%.
- 4.2 Main Results: Commercial systems significantly outperform the open-source framework on F2P step scores for requirements completion.
- 4.2 Main Results: P2P step scores exceed 98% on average, but P2P pass rates range from 70.0% to 88.3%, indicating frequent regressions after complex edits.
- 4.3 Step-level Analysis: The largest share of F2P outcomes falls below 30%, showing that failures concentrate in early task stages.
- 4.3 Step-level Analysis: Step-level scores expose where sequential workflows break and distinguish partial progress from failures in planning or specific logic implementation.
- 4.4 Self-Correction Capabilities: Self-correction improves F2P across rounds, but later rounds provide smaller marginal gains and can increase regression risk.
- 4.5 Human-Agent Collaboration: Plan injection outperforms self-correction in pass rate and efficiency; Claude Code reaches 58.3% versus 55.0%.
- 4.5 Human-Agent Collaboration: Interactive guidance generally exceeds static planning, while combining planning and interaction yields the best overall results and reduces interventions for Claude Code from 2.4 to 2.1.
5 Conclusion
LongCLI-Bench evaluates realistic long-horizon CLI programming through complex tasks, requirement and regression tests, and step-level scores. Current agents struggle, while human-agent collaboration substantially improves performance.
- LongCLI-Bench curates 20 complex tasks from over 1,000 workflows and assignments to evaluate long-horizon CLI programming.It targets real-world challenges beyond short, isolated benchmark tasks.
- The benchmark evaluates both requirement fulfillment and regression avoidance using step-level scores.This extends binary pass/fail evaluation with finer-grained task-completion information.
- Current agent systems achieve low pass rates on long-horizon tasks.
- Human-agent collaboration significantly boosts performance, motivating collaborative workflows alongside autonomous agent advances.
Limitations
LongCLI-Bench requires substantial manual effort to create high-quality tasks, resulting in a relatively small dataset. Its step-level metrics also omit some dimensions of agent performance, including code quality and efficiency.
- Each LongCLI-Bench task takes an average of 40 hours to create, contributing to the benchmark’s relatively small dataset.Creation includes requirements, solution paths, test environments, and test scripts.
- Step-level scores provide granular insights but do not fully assess code quality or efficiency.The benchmark could add metrics covering these dimensions in future versions.
Ethics Statement
The study uses publicly available or manually curated, anonymized data and reports human-agent collaboration under ethical safeguards. It also commits to transparency through releasing task-creation processes and evaluation results.
- LongCLI-Bench uses publicly available or manually curated data without sensitive or proprietary information.The data are anonymized to protect privacy.
- Human-agent collaboration follows ethical guidelines intended to protect participant rights and privacy.
- Evaluation data are standardized and anonymized for compliance with ethical research practices.
- The authors will release task-creation processes and evaluation results to support transparency and open participation.