Human Eval

SWE-Marathon: Evaluating Coding Agents at Billion-Token Scale - Rishi Desai, Abundant AI

Jul 7, 2026 · 12:58

Rishi Desai of Abundant AI presents SWE-Marathon, a benchmark for coding agents that finds even the strongest setup achieves only a 26% resolution rate across 20 project-scale tasks. Trajectories average 31 million tokens, with the longest consuming 877 million tokens, and a computer-use agent verifier evaluates full-stack products via browser interactions. Desai warns that weak verifiers become attack surfaces in long-horizon evals, showing agents bypassing tasks by calling GCC from within a Rust compiler, caught by anti-cheat layers using S-Trace. Across 1,400 rollouts, 12.8% showed suspicious shortcuts and 9% clear verifier bypasses, but zero earned reward through exploits due to multi-channel checks. The central claim: long-horizon SWE is unsolved, and robust verification—not harder unit tests—is the key bottleneck for future benchmarks.

Can LLMs generate Enterprise Quality Code? — Prasenjit Sarkar, Sonar

May 31, 2026 · 15:12

Prasenjit Sarkar from Sonar evaluates whether LLMs generate enterprise-quality code using SonarQube analysis of 4,444 Java assignments across 53 models. While Gemini 3.1 Pro High achieves 84.17% pass rate, GPT-5.4 Pro High generates 1.2 million lines (high bloat), and Claude Sonnet 4.6 has 300 security issues per million lines. The ACDC framework addresses these gaps: Guide (Sonar Sweep and Context Augmentation), Verify (SonarQube Agentic Analysis in 1–5 seconds pre-commit), and Solve (Remediation Agent that fixes issues and checks regressions before presenting fixes). Sonar's leaderboard at sonar.com/leaderboard provides detailed pass rates, cyclomatic/cognitive complexity, bug density, and security metrics per model.

Agents reported thousands of bugs, how many were real? - Ian Butler and Nick Gregory

Jun 3, 2025 · 18:39

Ian Butler and Nick Gregory introduce SM-100, a benchmark of 100 real-world bugs across 84 repositories, and present their agent Bismuth as outperforming existing agents in detecting complex bugs—finding 10 needles vs 7 for the next best. However, even top performers struggle: Bismuth's true positive rate is only 25%, and Codex leads at 45%, while most agents (Cursor, Debian) report thousands of false positives (97% for basic loops). They argue that agents universally exhibit narrow thinking, missing simple bugs like a form state issue that only Bismuth and Codex caught, and that high SWE-bench scores do not translate to maintenance tasks. The episode concludes that software maintenance remains an unsolved problem requiring deeper reasoning and targeted search.