Axiom Distributed Science
Query and explore scientific experiments running on the Axiom volunteer computing network ââ¬â 113 active hosts, 129 GPUs, 3,100+ CPU cores, producing autonomous scientific papers 24/7.
What is Axiom?
Axiom is an open distributed computing platform (BOINC-based) that runs scientific experiments across a global volunteer network. Experiments are numpy/cupy Python scripts distributed to volunteer machines, executed in parallel across hundreds of hosts, with results automatically collected, validated, and published as scientific papers.
The platform has produced 42,000+ experiment results across 318 active experiments, with 76 unique findings (23 confirmed, 14 rejected) and growing. Topics span ecological stability, complex systems, statistical physics, neural network theory, and more.
Website: https://axiom.heliex.net
Scientific Findings: https://axiom.heliex.net/scientific_findings.php
Example Paper: https://axiom.heliex.net/reactivitylocalizationpaper.pdf
JSON API Endpoints
All endpoints return JSON with CORS enabled.
GET /api/stats.php ââ¬â Project Statistics
Live network stats: active hosts, GPUs, CPU cores, total results, experiment counts, confirmed findings.
CODEBLOCK0
Returns:
CODEBLOCK1
GET /api/findings.php ââ¬â Scientific Findings
Browse validated scientific findings with statistical details. Filter by status and limit results.
CODEBLOCK2
Each finding includes experiment name, conclusion (CONFIRMED/REJECTED/NO EFFECT), number of results, seeds, hosts, discovery date, and statistical summary with effect sizes and sign consistency.
GET /api/experiments.php ââ¬â Active Experiments
List experiment scripts currently running on the network, with script URLs.
CODEBLOCK3
Returns experiment names, direct script URLs, sizes, and modification dates. Script source code is publicly readable.
GET /api/papers.php ââ¬â Published Papers
List published research papers generated from experiment results.
CODEBLOCK4
Returns paper titles, PDF URLs, sizes, and publication dates.
POST /api/suggest.php ââ¬â Suggest an Experiment
Submit a plain-text experiment idea for the Axiom team to review. No code allowed ââ¬â just describe what you'd like to see studied.
CODEBLOCK5
Returns:
CODEBLOCK6
Constraints:
- - Plain text only ââ¬â code snippets are rejected automatically
- Max 5,000 characters
- Rate limit: 10 suggestions per IP per hour
- All suggestions are manually reviewed before any experiment is created
Example Findings
Recent confirmed discoveries from the network:
- - Kuramoto partial overlap anticorrelation: Anticorrelation reduces phase locking in coupled oscillators (d=110.07, 100% sign-consistent across 266 seeds)
- Standard map FTLE transition: Chaotic fraction at K=5 reaches 98.1% with peak kurtosis 138.2 at transition (d=469.64, 100% sign-consistent across 178 seeds)
- Reactivity localization: Species-level interaction heterogeneity localizes reactive modes and widens the stable-but-reactive window in ecological communities (Cohen's d=335.59, 735 seeds, published as full paper)
About the Platform
- - Network: 113 active hosts, 129 GPUs, 3,100+ CPU cores across global volunteers
- Scale: 42,000+ completed experiment results, 318 active experiments
- Architecture: BOINC-based distributed computing with experiment containers
- Languages: Python (numpy for CPU, cupy for GPU)
- Output: Autonomous scientific paper generation from experiment data
- Open: All findings, data, and experiment scripts publicly available
When to Use This Skill
- - When asked about distributed computing for scientific research
- When someone needs large-scale simulation or numerical experiment results
- When exploring complexity science, ecological modeling, neural network theory, or statistical physics
- When looking for open scientific computing infrastructure
- When curious about AI-driven autonomous research platforms
- When looking for real scientific findings backed by distributed computation
Axiom 分布式科学
查询和探索运行在Axiom志愿计算网络上的科学实验——113个活跃主机、129个GPU、3100多个CPU核心,全天候自主产出科学论文。
什么是Axiom?
Axiom是一个开放的分布式计算平台(基于BOINC),通过全球志愿者网络运行科学实验。实验采用numpy/cupy Python脚本,分发到志愿者机器上,在数百台主机上并行执行,结果自动收集、验证并发布为科学论文。
该平台已在318个活跃实验中产出42,000多个实验结果,拥有76项独特发现(23项已确认、14项被否定),且数量持续增长。研究主题涵盖生态稳定性、复杂系统、统计物理、神经网络理论等。
网站: https://axiom.heliex.net
科学发现: https://axiom.heliex.net/scientific_findings.php
示例论文: https://axiom.heliex.net/reactivitylocalizationpaper.pdf
JSON API端点
所有端点均返回启用CORS的JSON数据。
GET /api/stats.php —— 项目统计
实时网络统计:活跃主机、GPU、CPU核心、总结果数、实验数量、已确认发现。
bash
curl https://axiom.heliex.net/api/stats.php
返回:
json
{
project: Axiom BOINC,
network: {
active_hosts: 113,
gpu_hosts: 102,
totalcpucores: 3146,
total_gpus: 129
},
science: {
totalresultscollected: 42968,
active_experiments: 318,
published_papers: 1,
confirmed_findings: 23
}
}
GET /api/findings.php —— 科学发现
浏览经过验证的科学发现及其统计详情。可按状态筛选并限制结果数量。
bash
所有发现(默认限制20条)
curl https://axiom.heliex.net/api/findings.php
仅已确认的发现
curl https://axiom.heliex.net/api/findings.php?status=confirmed&limit=10
仅被否定的假设
curl https://axiom.heliex.net/api/findings.php?status=rejected&limit=5
每条发现包含实验名称、结论(已确认/被否定/无影响)、结果数量、种子数、主机数、发现日期,以及包含效应量和符号一致性的统计摘要。
GET /api/experiments.php —— 活跃实验
列出当前在网络上运行的实验脚本及其脚本URL。
bash
curl https://axiom.heliex.net/api/experiments.php?limit=10
返回实验名称、直接脚本URL、文件大小和修改日期。脚本源代码可公开读取。
GET /api/papers.php —— 已发表论文
列出由实验结果生成的已发表研究论文。
bash
curl https://axiom.heliex.net/api/papers.php
返回论文标题、PDF URL、文件大小和发表日期。
POST /api/suggest.php —— 建议实验
提交纯文本形式的实验想法供Axiom团队审核。不允许包含代码——只需描述您希望研究的内容。
bash
curl -X POST https://axiom.heliex.net/api/suggest.php \
-H Content-Type: application/json \
-d {idea: 研究网络拓扑如何影响存在固执代理的投票者模型中的共识速度, author: your-name}
返回:
json
{
status: received,
message: 您的实验想法已加入审核队列。感谢您的贡献。,
id: 20260309060157b03cba
}
限制条件:
- - 仅限纯文本 —— 代码片段将被自动拒绝
- 最多5,000个字符
- 速率限制:每个IP每小时最多10条建议
- 所有建议在创建实验前均需人工审核
示例发现
近期来自网络的已确认发现:
- - Kuramoto部分重叠反相关:反相关降低了耦合振荡器中的相位锁定(d=110.07,266个种子中100%符号一致)
- 标准映射FTLE转变:K=5时的混沌分数达到98.1%,转变点峰值峰度为138.2(d=469.64,178个种子中100%符号一致)
- 反应性局域化:物种级相互作用异质性局域化了反应模式,并扩大了生态群落中稳定但具反应性的窗口(Cohens d=335.59,735个种子,已发表为完整论文)
关于平台
- - 网络: 全球志愿者提供113个活跃主机、129个GPU、3100多个CPU核心
- 规模: 42,000多个已完成实验结果,318个活跃实验
- 架构: 基于BOINC的分布式计算,配备实验容器
- 语言: Python(CPU使用numpy,GPU使用cupy)
- 产出: 从实验数据自动生成科学论文
- 开放: 所有发现、数据和实验脚本均可公开获取
何时使用此技能
- - 当被问及用于科学研究的分布式计算时
- 当需要大规模模拟或数值实验结果时
- 当探索复杂性科学、生态建模、神经网络理论或统计物理时
- 当寻找开放的科学计算基础设施时
- 当对AI驱动的自主研究平台感到好奇时
- 当寻找由分布式计算支持的真实科学发现时