Danus 是一个数学推理代理编排系统,由主代理(Claude Code)指挥一群自主工作代理执行证明,通过冷启动验证器把关正确性,并将验证结果存入内容寻址的事实图,最终生成可读报告或可发表的 LaTeX 论文。

Stars

106

7 天增长

暂无数据

Fork 数

25

开放 Issue

0

开源协议

Apache-2.0

最近更新

2026-07-22

AI 仓库情报摘要
FR-AI / ANALYSIS

为什么值得关注

该系统通过角色隔离工具强制权力分离,使用无状态验证器保证每个提交的公正性,借助并行工作代理扩展证明搜索,并在事实图基础上自动验证最终论文的准确性。

适合谁使用

  • 自动定理证明与形式化数学的研究人员
  • 需要处理大型或复杂证明的数学家
  • 研究对齐、验证驱动的多代理架构的 AI 安全专家
  • 构建具有可验证记忆的多代理推理系统的开发者

典型使用场景

  • 证明具有长依赖链的研究级数学定理
  • 并行探索多种证明策略并累积已验证的引理
  • 直接将已验证的证明结果生成可发表的 LaTeX 论文
  • 通过不可篡改、依赖关系可追溯的事实图审计和扩展数学成果

项目优势

  • 角色隔离的 MCP 服务器阻止未经验证的条目进入事实图
  • 无状态验证器保证每次提交都被全新判断,无记忆污染
  • 内容寻址事实图支持级联撤销和深度依赖追踪(一次实际运行中记录了 3,157 个事实和 8,616 条边)
  • 最终论文在交付前会针对事实图重新验证,叠加在逐事实验证之上

使用前须知

  • 需要自带 API 密钥和兼容 OpenAI 的 codex 后端,产生 Token 费用
  • 用户需提前定义“完成”条件,避免在困难问题上无限消耗 Token
  • 论文生成质量高度依赖于提供优质示例论文作为参照

README 快速开始

Danus: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory

Danus orchestrates mathematical reasoning agents with fact-graph memory. A main agent (Claude Code) steers a swarm of autonomous codex workers that prove; a cold-start verifier is the sole authority on correctness: a result becomes real only once it passes. Verified results accumulate in a content-addressed fact graph — the system's only source of truth — and a strategy loop (a strong reasoning model) decomposes the problem and steers the swarm. When you have the answer, Danus renders it into a human report or a publishable LaTeX paper.

Danus builds on the worker–verifier core of our earlier system Rethlas (arXiv:2604.03789). The paper and the technical report tell the full story: the system, six research-level case studies it resolved, and what we learned along the way.

See ARCHITECTURE.md for the layered design and the map of every module.

How it works

The design follows a strict separation of powers: the main agent performs the global planning and coordination, the workers carry out the detailed proof search, the verifier is the sole authority on correctness, and the fact graph holds every verified result and is the system's only source of truth.

Each kind of agent carries out its role through its own skills and its own role-gated set of tools, so the separation is enforced by construction, not by prompts: the main agent has no fact_submit (the agent that steers the search structurally cannot introduce unverified mathematics into the fact graph), and the verifier writes nothing at all.

Every claim enters truth through one cycle:

A worker typically focuses on one claim at a time — a lemma, a counterexample, a toy example — rather than an entire proof. It repeatedly submits the claim with a supporting proof and revises it under the verifier's feedback until it passes, at which point the claim enters the fact graph as a fact, with the facts its proof depends on as its incoming edges. The verifier is stateless: a fresh instance judges each submission and retains nothing afterwards. Because each worker draws on only the facts it needs for its current claim and submits one fact at a time, the working

项目描述

Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory

相关仓库与替代方案

根据分类、Topic 和编程语言匹配的相似项目。

makecindy
精选
makecindy GitHub avatar

cindy

Cindy is an open-source AI agent that runs locally on your machine, integrates multiple AI harnesses and models, and provides memory, skills, and automation to perform real work in your projects and apps.

AI 与机器学习大语言模型
958
7-e1even
精选
7-e1even GitHub avatar

learn-agent

A collection of notes on building coding agents, derived from the production development of the desktop agent Reina, with each mechanism simplified into a zero-dependency, single-file Node.js demo.

AI 与机器学习大语言模型
218
S40911120
精选
S40911120 GitHub avatar

recensa

Recensa is a self-hosted web viewer that indexes Claude Code session transcripts into a local SQLite database, enabling full-text search, replay, and audit of all past agent conversations without uploading data anywhere.

AI 与机器学习大语言模型
67