arXiv：cs.AI· Yi Wang, Haopeng Zhang, Chengxiang Huang, Rui Dai, Kaikui Liu, Piotr Koniusz, Xiangxiang Chu·· 5 小时前
LoopArena：把模型作为循环工程的运行时控制器进行基准测试
LoopArena: Benchmarking Models as Runtime Controllers for Loop Engineering
AI 导读
LoopArena 是一个评估模型能否引导独立编码智能体完成长时任务的基准，被评测模型担任 Controller，在每轮编码后根据结构化摘要指示固定的 Worker 执行或验证下一步，或决定停止。
正文
Abstract:Loop Engineering is emerging as a practice for organizing development work around coding agents. Instead of writing each prompt by hand, practitioners design loops that monitor progress, assign work, run checks, and decide what the agent should do next. Even with a capable coding agent, a loop may trust a stale progress note, skip needed verification, spend its budget in the wrong direction, or stop before the task is safe to submit. Yet the final outcome of one end-to-end run cannot tell whether success or failure reflects the loop's guidance or the coding agent's ability to carry out the task. We introduce LoopArena, a benchmark for evaluating how well one model can guide a separate coding agent through a long-running task. The model under evaluation is the Controller: after each coding round, it receives a structured summary of the run and instructs a separate, fixed coding agent, the Worker, on what to do or verify next, or decides whether to stop. LoopArena evaluates this ability in three complementary settings that differ in execution scope and cost. Type I consists of 160 low-cost control questions; its execution cost is incurred once during benchmark construction, so each new Controller is evaluated through a single four-way choice with no code execution. Type II evaluates Controller guidance on 80 selected task slices, while Type III evaluates the corresponding 80 full tasks from their original states. On full tasks, the best observed Strict Success Rate is 36.25%, leaving substantial room for improvement in long-horizon loop control. Across Controllers, the paired reduction in estimated inference cost averages 60.6%, and Type II produces a positively correlated ordering under the main Core criterion (Spearman's rho = 0.7353). We release the benchmark data and evaluation code at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.28281 [cs.AI] |
| (or arXiv:2608.28281v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.28281 arXiv-issued DOI via DataCite |
Submission history
From: Chengxiang Huang [view email]
[v1]
Fri, 28 Aug 2026 12:44:54 UTC (626 KB)
[v2]
Tue, 13 Oct 2026 06:57:13 UTC (612 KB)
来源：arXiv：cs.AI · arxiv.org