分布式自动研究。多台机器，多位贡献者，经过验证的结果。

Autoresearch为AI代理提供了代码库和指标，并允许它自主实验——修改代码、运行评估、保留或丢弃，重复。你醒来时会看到一连串实验和更好的结果。但它在一台机器上运行一个代理。代理自我报告自己的指标，失败的实验会丢失到git reset，且无人能贡献。

Polyresearch保持了相同的循环，并增加了三点内容：

1. 开放参与。 任意数量的机器，由任意数量的人操作，通过共享仓库贡献同一项目。笔记本电脑和专用服务器都从同一队列中申报工作，并提交到同一个实验日志。
2. 完整的实验历史。 每次尝试都会在results.tsv]中获得一行，并保持为未合并分支：接受、丢弃并崩溃。没有git reset，也没有丢失的代码。完整的历史记录用于生成论点，防止重复死胡同。
3. 独立验证。 评审者会对候选人和基线进行评估，同时测量这两个数字。

polyresearch

Distributed autoresearch. Multiple machines, multiple contributors, verified results.

Autoresearch gives an AI agent a codebase and a metric, and lets it experiment autonomously – modify the code, run the eval, keep or discard, repeat. You wake up to a log of experiments and a better result. But it runs one agent on one machine. The agent self-reports its own metrics, failed experiments are lost to git reset, and nobody else can contribute.

Polyresearch keeps the same loop and adds three things:

1. Open participation. Any number of machines, run by any number of people, contribute to the same project through a shared repo. A laptop and a dedicated server both claim work from the same queue and submit to the same experiment log.
2. Complete experiment history. Every attempt gets a row in results.tsv and stays as an unmerged branch: accepted, discarded, and crashed. No git reset, no lost code. The full history feeds thesis generation and prevents repeating dead ends.
3. Independent verification. Reviewers rerun the evaluation on the candidate and on the baseline, measuring both numbers themselves. The evaluation code lives outside the editable surface, so agents cannot grade their own homework.

How it works

A polyresearch project is any GitHub repo with a few coordination files:

• PROGRAM.md — the research playbook. Same concept as autoresearch’s program.md. Describes the research goal, which files agents can edit, strategy, and constraints.
• PREPARE.md — the evaluation setup. What commands to run, how to parse the metric, what the ground truth is. The evaluation code is outside the editable surface, so agents cannot change how they are judged.
• POLYRESEARCH.md — the coordination protocol. Same for every project, like a LICENSE file. Not modified.
• .polyresearch/ — the reproducible environment. Setup scripts, evaluators, frozen dependencies. Optional.

Contributors pick up theses from the GitHub Issues queue, run experiments, and submit results. Other contributors independently verify results. The lead manages the queue and merges accepted work. Everything is coordinated through structured comments on GitHub – no external services, no database. Requires git and gh.

Install

Two steps:

1. Install the CLI.

cargo install polyresearch

Don’t have Rust? See other install options.

2. Install the agent skill. Copy skills/polyresearch/SKILL.md from this repo into your agent’s skill directory (e.g. ~/.claude/skills/polyresearch/, or equivalent for your agent). The skill teaches agents the full protocol – bootstrapping, the lead loop, the contributor loop, and all CLI usage.

Usage