mlsanityData to 7 Oct 2026

Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Instead

arXiv 2609.17394 ↗First posted 15 Sep 2026cs.SE cs.AIcited by 0

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

Abstract (arXiv)

Small differences on coding-agent leaderboards are often read as an ordering of systems. We audit whether the published verdicts support this reading, using 254 SWE-bench submissions across four splits without running models. On Verified, the leading two entries each resolve 396 of 500 instances. The top ten share 285 successes and 51 failures, leaving 164 instances that distinguish their outcomes. Frontier solution sets have median nesting 0.935 against a score-implied baseline of 0.774, indicating strongly shared successes. Scores also depend on the evaluated model-scaffold pair: observed within-model scaffold ranges reach 29.8 percentage points, compared with the 8.8-point spread of the top thirty. Six of nine cell-mean interaction tests remain significant after Holm correction, although this observational design does not identify causal scaffold effects. Exact paired McNemar tests separate none of the 29 adjacent Verified top-thirty pairs at alpha=0.05, while the larger Test split separates 14 of 23. A stated leader-based rule yields three descriptive tiers, or two after Holm correction; non-rejection does not establish equivalence. We release the partition and a five-step audit protocol that profiles shared outcomes, tests paired differences, reports grouping sensitivity, and estimates the instance budget needed for resolution. The results motivate reporting comparison-set-specific resolution and model-scaffold provenance instead of interpreting small aggregate gaps as established rank differences.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 23 references that are in the map. First posted on arXiv 15 Sep 2026.

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13 of 23 references.

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  1. 2026Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering16 Jun

Other references · 12

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2024tinyBenchmarks: evaluating LLMs with fewer examplescited by 295
  3. 2020With Little Power Comes Great Responsibilitycited by 138
  4. 2024SWE-Bench+: Enhanced Coding Benchmark for LLMscited by 95
  5. 2025Establishing Best Practices for Building Rigorous Agentic Benchmarkscited by 94
  6. 2023Efficient Benchmarking of Language Modelscited by 71
  7. 2025The SWE-Bench Illusion: When State-of-the-Art LLMs Remember Instead of Reasoncited by 47
  8. 2025Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theorycited by 42
  9. 2026AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluationcited by 13
  10. 2026Automated Benchmark Auditing for AI Agents and Large Language Modelscited by 10
  11. 2026Resolution Diagnostics for Paired LLM Evaluationcited by 6
  12. 2026Rank Intervals for Leaderboards: A Hierarchical Framework for Model Evaluationcited by 5

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  4. 2026The Growing Pains of Frontier Models: When Leaderboards Stop Separating and What to Measure Next
  5. 2026Identical Runs, Different Results: Benchmarking AI Coding Agents on Open-Weight Models
  6. 2026Beyond Static Leaderboards: Predictive Validity for the Evaluation of LLM Agents
  7. 2026The Double Measurement Confound in Agent Benchmarks: De-Scaffolding, Ground-Truth Scoring, and Reliability Beyond the Mean
  8. 2026EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions