6arXiv cs.AI (Artificial Intelligence)·
PAIChecker finds 13.6% misalignment in SWE-bench Verified instances, proposes multi-agent fix
A new arXiv paper systematically audits SWE-bench Verified and finds that 13.6% of PR-Issue pairings exhibit misalignment across five patterns and eleven fine-grained scenarios, undermining the benchmark's validity as an LLM evaluation tool. The authors introduce PAIChecker, a three-phase multi-agent system for detecting such misalignment, achieving up to 92.12% binary accuracy on SWE-Gym and 91.67% on SWE-bench Multilingual. The finding is significant because SWE-bench is one of the most widely cited benchmarks for agentic coding capability, and systematic data quality issues could distort leaderboard rankings and capability claims.
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Audit finds GSO, SWE-Perf, and SWE-fficiency benchmarks unreliable for measuring coding agent progress
A new arXiv paper audits three prominent repository-level code-optimization benchmarks (GSO, SWE-Perf, SWE-fficiency) used to rank coding agents, finding significant reliability problems across all three. Reference patches satisfy validity rules in cross-machine replays for only 39/102 GSO tasks and 11/140 SWE-Perf tasks, and leaderboard rankings disagree on 9 of 28 pairwise comparisons depending on scoring rule choice. The authors also find that at least one public submission already matches or beats the reference patch on 85.3% of replay-valid tasks, suggesting aggregate leaderboard scores obscure the true frontier. The study raises substantive concerns about whether these benchmarks are providing reliable signal for claims of coding-agent capability progress.
OpenAI analysis reveals reliability issues in SWE-Bench Pro coding benchmark
OpenAI published an analysis identifying problems with SWE-Bench Pro, a widely-used benchmark for evaluating AI coding capabilities. The post raises concerns about the benchmark's reliability and accuracy as a signal for model performance. This matters because SWE-Bench is a primary reference point for comparing frontier coding models, and benchmark integrity directly affects how labs and practitioners interpret capability claims.
Introducing SWE-bench Verified
OpenAI is releasing SWE-bench Verified, a human-validated subset of the SWE-bench benchmark designed to more reliably evaluate AI models on real-world software engineering tasks. The original SWE-bench contained issues that were ambiguous or unsolvable, leading to unreliable scores; the Verified subset addresses this by having human annotators confirm task solvability and clarity. This provides a cleaner signal for comparing coding agent performance across labs.
OpenAI Abandons SWE-bench Verified Over Contamination and Measurement Flaws
OpenAI has announced it will no longer evaluate models on SWE-bench Verified, citing benchmark contamination and flawed test cases that cause it to mismeasure frontier coding capabilities. Their analysis identified both problematic test design and training data leakage as sources of unreliability. OpenAI recommends SWE-bench Pro as a replacement benchmark for evaluating coding progress.
Claw-SWE-Bench: A benchmark for evaluating agent harnesses on multilingual coding tasks
Researchers introduce Claw-SWE-Bench, a multilingual SWE-bench-style benchmark and adapter protocol designed to fairly compare heterogeneous agent harnesses ("claws") on GitHub issue-resolution tasks. The benchmark contains 350 instances across 8 languages and 43 repositories, with an 80-instance Lite subset for cost-efficient validation. Key findings show adapter design dominates raw model choice: a minimal adapter scores 19.1% Pass@1 versus 73.4% for a full adapter using the same GLM 5.1 backbone, and harness choice and model choice each shift Pass@1 by roughly 27-29 percentage points. The work also introduces cost accounting as a first-class evaluation axis alongside accuracy.
Automated Benchmark Auditing for AI Agents and Large Language Models (ABA)
The paper introduces Auto Benchmark Audit (ABA), an agentic framework that systematically audits AI benchmark tasks for issues such as ambiguous specifications, environment conflicts, and incorrect ground truths. Applied to 168 benchmarks across nine domains including NeurIPS publications, ABA identifies critical issues in over 25.7% of evaluated tasks. The authors demonstrate that filtering out flawed tasks materially shifts model rankings and improves average performance on SWE-bench Verified and Terminal-Bench 2 by 9.9% and 9.6% respectively, indicating that current benchmark scores are significantly distorted by task quality problems. The agentic tool and annotations are released publicly.
DeepSWE, ProgramBench, and ITBench-AA emerge as harder successors to SWE-bench for agent evaluation
Three new benchmarks — DeepSWE (by Datacurve), ProgramBench (Meta/Stanford/Harvard), and ITBench-AA (IBM/Artificial Analysis) — are positioned as more rigorous replacements for the SWE-bench family, which models have largely saturated. DeepSWE tests feature implementation using private codebases and human-written problems; ProgramBench evaluates agents' ability to recreate functional programs from scratch; ITBench-AA measures root-cause diagnosis in real-world IT incident scenarios. Current top performers include GPT-5.5 (70% on DeepSWE), Claude Opus 4.7 (46.7% on ITBench-AA), and Claude Opus 4.7 (3% on ProgramBench at the 95% pass threshold), illustrating that even frontier models have substantial headroom.
SWE-Explore: New benchmark isolates repository exploration capability in coding agents
SWE-Explore is a new benchmark targeting repository exploration as a distinct, fine-grained capability of coding agents, separate from end-to-end task resolution. It covers 848 issues across 10 programming languages and 203 open-source repositories, with line-level ground truth derived from successful agent trajectories. Evaluation across retrieval methods, coding agents, and specialized localizers finds that agentic explorers outperform classical retrieval, and that line-level coverage and efficient ranking remain the key differentiators at the frontier. The benchmark addresses a gap in SWE-bench-style evaluations that treat task resolution as a binary outcome.