TestJack is a scalable framework that audits large language model (LLM) coding agents by generating new tests for each trial, focusing on prompt requirements that may be violated. It keeps only the tests passed by the ground‑truth patch and re‑examines failures, providing replayable evidence for each confirmed error. In experiments across six model backends and five benchmarks, TestJack found that 34.4% of trials deemed correct actually violated task requirements, reducing the overall resolution rate from 50.6% to 33.2%.
By Shuangjie Yao, Hao Wang, Koushik Sen, Simin Chen, Baishakhi Ray, Dawn Song
TEX is a new test‑time scaling framework that uses execution‑driven cross‑validation to generate and refine tests for language‑model agents. By running agents in parallel and cross‑executing each test against every patch, TEX builds a $K imes K$ feedback matrix that guides objective refinement without sharing verbose traces. On the SWT‑Bench Verified benchmark, TEX reaches 86.4% accuracy—setting a new state‑of‑the‑art and improving over single‑agent baselines by 9.0%—and even random selections from its outputs outperform the previous best score.
By Akash Gokul, Srijan Bansal, Yingbo Zhou, Semih Yavuz
SWE‑Duel proposes a self‑scaling benchmark for coding agents, where tasks and oracles are generated at evaluation time to prevent data leakage and adapt difficulty to participant performance. In the arena, two agents take turns as Red and Blue: Red proposes a feature, injects a subtle bug, and must repair it blind, while Blue reviews the pull request without knowing the bug and must pass all tests. A tournament of 11 model‑harness participants on 15 repositories across five languages showed GPT‑5.5/Codex CLI as the top solver and Claude Opus 4.8/mini‑swe‑agent as the top task author, with newer model versions producing harder tasks for older ones.
By Wei Minn, Zhensu Sun, Lwin Khin Shar, David Lo
SWE‑Duel proposes a self‑scaling benchmark for coding agents, where tasks and oracles are generated at evaluation time to prevent data leakage and adapt difficulty to the agents’ capabilities. In the arena, two agents take turns modifying a real repository: the Red agent adds a feature, introduces a subtle bug, and the Blue agent must fix it without knowing the bug exists. A tournament of 11 model‑harness participants on 15 repositories in five languages showed GPT‑5.5 as the best solver and Claude Opus 4.8 as the best task author, with newer model versions producing harder tasks for older ones.
arXiv:2608. 16742v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging.
By Hongyue Yu, Kefan Li, Jiakun Li, Hongzheng Chai, Yuan Yuan, Rui He, Junyi Wei
SWEPatchTest is a benchmark that evaluates software tests by measuring their ability to reproduce issues, accept correct patches, and reject incorrect ones across 619 tasks and 82,579 candidate patches. It introduces the Joint Validation Rate metric, revealing that even the best configurations only achieve 9.21% success when all three criteria are met. Using these validation scores to select demonstrations for supervised fine‑tuning can roughly triple the Joint Validation Rate and enhance downstream repair performance on multiple SWE‑bench datasets.
By Hua Lu, Pengcheng Wang, Han Li, Zichen Tian, Yanyu Chen, Huang He, Bingjin Chen