OpenClaw与Hermes Agent性能对比实验：验证成本决定表现
OpenClaw 2.0 vs Hermes experiment by @atomicbot_ai is a good example of why the…
OpenClaw 2.0 vs Hermes experiment by @atomicbot_ai is a good example of why the model alone tells you very little about agent performance.
@atomicbot_ai 进行的 OpenClaw 2.0 与 Hermes 实验很好地说明了，仅凭模型本身很难判断智能体的性能。
Outputs: OpenClaw 2.0: ~2.1M tokens, ~$4.5, 10 self-fixes Hermes Agent: ~2.9M tokens, ~$4, 20 self-fixes
输出结果： OpenClaw 2.0：约 210 万 token，约 4.5 美元，10 次自我修复 Hermes Agent：约 290 万 token，约 4 美元，20 次自我修复
The difference was in how they checked themselves.
差异在于它们的自检方式。
Atomic Bot lets you run AI agents in the cloud without managing the setup yourself.
Atomic Bot 允许你在云端运行 AI 智能体，而无需自行管理环境配置。
In this experiment
在本次实验中
- OpenClaw screenshotted frames at specific timestamps, counted pixels, then corrected visual errors such as an inverted coat-tail direction. - Hermes went further and created its own render-checking setup, measuring frame timing, testing whether loop seams were visible, and auditing scenes against the original spec.
- OpenClaw 在特定时间戳截取帧画面、统计像素数，然后修正视觉错误，例如反转的衣摆方向。 - Hermes 更进一步，构建了自定义的渲染检查流程，测量帧时间、测试循环接缝是否可见，并根据原始规范审查场景。
That extra ~800K tokens was largely verification work.
这多出的约 80 万 token 主要用于验证工作。
We usually talk about agent tokens as inference cost.
我们通常将智能体的 token 消耗视为推理成本。
This run is a nice reminder that some of those tokens are effectively a QA budget. Once agents can generate their own checks, the important question becomes how much compute you want them spending proving their work before returning it.
这次运行很好地提醒了我们，其中部分 token 实际上构成了质量保证（QA）预算。一旦智能体能够生成自身的检查机制，关键问题就变成了：你希望它们在返回成果前投入多少算力来证明其工作的正确性。
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力