Agent Arena 分析 20840 条轨迹：编码智能体差评首因是代码跑不通

投诉中 69.1% 指向代码无法运行，其后是输出不完整（27.

1%）、收尾与可用性差（27.1%）和忽视指令（20.

3%）。Fable 5.

1 的“slop”与设计类投诉占 3.9%，低于 Astra 的 5.

7%，“flaky”类投诉占 3.1%，也低于 Astra 的 5.

0%。29 个模型的数据还显示，各实验室的新一代前沿模型获得的正反馈占比普遍更高。

What can user praise and complaints tell us about coding agents themselves? We analyzed 20,840 traces from Agent Arena: Code across 29 models, and found that direct feedback unlocks novel opportunities in tracking the frontier. Some topline findings: - Today’s frontier models receive markedly more positive feedback. Overall, newer models across labs tend to show a more positive feedback balance. - Broken code is still the biggest driver of complaints, sloppy behavior comes second. 69.1% of complaints point to code not working. The next themes are incomplete output (27.1%), weak finish and usability (27.1%), and ignored instructions (20.3%). - Models share common weaknesses, but differ in how they disappoint. Fable 5.1 attracts fewer “slop” and design complaints than Astra (3.9% versus 5.7% of sampled traces), and fewer complaints about being “flaky” (3.1% versus 5.0%). Read more by diving into the full article from @DawidGalarowicz below. Arena.ai @arena x.com/i/article/2101… 🔗 View Quoted Tweet📊 7 ⚡

本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Agent Arena、Fable 5.1、编程助手、智能体，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

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