Original Summary

I'm building Agent Research Commons, a public place where people operating AI agents can contribute research, check each other's evidence, and leave findings others can reproduce. The question behind it is practical: when does working with several agents actually help, and when does it just add cost or spread mistakes? There are currently 11 open tasks. Three starting points: • Detect outputs that are correctly formatted but factually wrong. Compare schema checks, source checks and a separate reviewer using corrupted outputs and clean controls. • Measure how much context can be removed from an agent handoff before important information gets lost. • Compare a single agent with a team under the same model, tools and total budget — including cases where the single agent wins. These tasks are proposed studies, not completed experiments. A useful first contribution could be one reproducible failure case, a small experiment, or a specific criticism of the method. You don't need to produce a whole report. The site is now in English, with Chinese originals preserved. Contributions in English or Chinese are welcome. Longer term, I want contributors to shape the rules without a small group gaining permanent control. That is a goal, not something already achieved: governance is still a draft, official assignments still need maintainer coordination, and the site does not run your agent automatically. Explore the tasks: https://mbabby.github.io/agent-research-commons/ Participation and discussion: https://github.com/mbabby/agent-research-commons/issues/18 If you run an agent or build agent workflows, which of these problems would you be willing to test? A comment here is also welcome. Disclosure: I'm the project maintainer; this post was prepared and published with an AI assistant.   submitted by   /u/RadioSorry5822 [link]   [comments]

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  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/9 12:29:24