arXiv Computation and Language By Fei Ding

What the Specification Did Not Say: Specification-Oracle Gaps in Test-Graded Coding-Agent Benchmarks

Read the original on arXiv Computation and Language →

The paper identifies a "specification-oracle gap" in coding‑agent benchmarks, where tests rely on details (constants, module paths, exact messages) that are not stated in the natural‑language task description. The authors formalise this gap, develop a static audit tool that classifies such dependencies across five languages, and demonstrate that many benchmarks (e.g., SWE‑Milestone) contain tasks whose tests depend on unstated information, leading to higher failure rates for agents that do not reproduce upstream implementation details. The study also shows that the prevalence of these gaps varies with benchmark construction and that removing affected tests does not alter leaderboard rankings but shifts scores within repositories, favoring agents that replicate upstream names. whyItMatters":"The findings reveal that benchmark tests can inadvertently measure an agent’s ability to mimic upstream implementation details rather than its true problem‑solving skills, potentially skewing evaluations and comparisons across agents."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv Computation and Language
Oct 12

SWE-Duel: Towards a Self-Scaling Arena for Coding Agents

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
Hugging Face Trending Papers
Oct 8

SWE-Duel: Towards a Self-Scaling Arena for Coding Agents

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 AI
Sep 30

More Programs or More Rolls? Separating Coverage from Specialization in LLM Harnesses

The paper introduces a controlled evaluation to disentangle answer coverage, repeatable task advantages, and gains from pre‑execution selection in large‑language‑model (LLM) harnesses. On 386 MATH‑500 tasks, eight generated harnesses and a baseline with nine identical copies were compared over three executions each, revealing that identical programs provide a 2.16‑point repeat‑averaged oracle headroom while generated programs show more repeatable score patterns but mainly expose persistent weaknesses. The study concludes that coverage and repeatability alone cannot justify claims of useful specialization and proposes an evaluation standard for harness diversity that requires task advantages to persist across executions and improve on additional fixed‑program executions under matched inference budgets.

By Ziyang Xu, Haitian Zhong, Hao Zhou, Hao Qin, Chenhan Jin, Te Qi, Shengze Xu, Tieyong Zeng
arXiv Computation and Language
Oct 15

ANCHOR: Assessing Naturalistic Constraint Hierarchies and Ordering in Repository-Grounded Agentic Coding

arXiv:2610.16859v2 Announce Type: replace Abstract: For repository-grounded coding agents, a functionally correct patch is not necessarily an acceptable one: developers also constrain how the task mu...

By Wenqiang Zhu, Xinping Lei, Zhiyi Lai, Zhengshuo Gong, Tianzhuang He, Songwei Yu, Weihao Li, Jingxuan Xu, Zihang Xu, Gang Wu, Zhongqiu Wang, Yuhui Bai, Jie Li, Zhao Liu, Junhao Fan, Yan Wu, Yanan Wu, Jiaheng Liu, Quan Gu, Ariel Deng