mlsanityData to 7 Oct 2026

ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents

arXiv 2605.20251 ↗First posted 18 May 2026cs.SE cs.AIcited by 5

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Existing benchmarks for LLM coding agents primarily evaluate final outcomes. While useful for measuring overall capability, these metrics provide limited visibility and often miss defects that arise during execution. We present ProcCtrlBench, a benchmark for execution-process evaluation in LLM coding agents. ProcCtrlBench organizes recurrent execution defects into a reusable ontology covering 11 defect types in 4 categories, and evaluates agent trajectories through standardized process evidence rather than final outcomes alone. To support comparison across heterogeneous agents, ProcCtrlBench standardizes raw logs into a unified trajectory representation and reports calibrated scorecards over process-level findings. In addition, ProcCtrlBench uses control preservation as a way to quantify execution-process quality, capturing whether execution remains interpretable, interruptible, correctable, reversible, and able to hand back authority when needed. We evaluate ProcCtrlBench on 200 cases sampled from three benchmarks: AndroidBench, TerminalBench, and SWE-bench-Verified. Results show that ProcCtrlBench can be instantiated with useful reliability, provides more stable semantics than direct thresholding, and reveals meaningful differences in execution quality that are often overlooked by conventional outcome-based evaluation.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 32 of its 37 references that are in the map. First posted on arXiv 18 May 2026.

Builds on

32 of 37 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Why Do Multi-Agent LLM Systems Fail?17 Mar
  2. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul

Other references · 30

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  4. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  5. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  6. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  7. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
  8. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 488
  9. 2021Dynabench: Rethinking Benchmarking in NLPcited by 447
  10. 2023Generating with Confidence: Uncertainty Quantification for Black-box Large Language Modelscited by 286
  11. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
  12. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
18 more
  1. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 115
  2. 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarkscited by 102
  3. 2025Establishing Best Practices for Building Rigorous Agentic Benchmarkscited by 94
  4. 2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 78
  5. 2024Calibrating Large Language Models Using Their Generations Onlycited by 63
  6. 2025Position: Evaluating Generative AI Systems Is a Social Science Measurement Challengecited by 61
  7. 2023TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Modelscited by 59
  8. 2025Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluationcited by 57
  9. 2024A Survey of Uncertainty Estimation in LLMs: Theory Meets Practicecited by 54
  10. 2025Measurement to Meaning: A Validity-Centered Framework for AI Evaluationcited by 54
  11. 2024Multicalibration for Confidence Scoring in LLMscited by 44
  12. 2025Toward an Evaluation Science for Generative AI Systemscited by 42
  13. 2024Towards interactive evaluations for interaction harms in human-AI systemscited by 27
  14. 2024A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impactscited by 13
  15. 2024ProcBench: Benchmark for Multi-Step Reasoning and Following Procedurecited by 12
  16. 2024A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluationscited by 6
  17. 2025Limits of Safe AI Deployment: Differentiating Oversight and Controlcited by 3
  18. 2026CIRCLE: A Framework for Evaluating AI from a Real-World Lenscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair
  2. 2026Beyond Task Completion: Revealing Corrupt Success in LLM Agents through Procedure-Aware Evaluation
  3. 2026PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization
  4. 2026Failure as a Process: An Anatomy of CLI Coding Agent Trajectories
  5. 2026EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions
  6. 2026TraceLab: Characterizing Coding Agent Workloads for LLM Serving
  7. 2026LoopsBench: From Harness Engineering to Loop Engineering in Coding Agent Evaluation
  8. 2026Evaluating Agentic Code Repair Capabilities in Distributed Systems