The Origins of DNAi – From Research to Revolution

DNAi began in 2024 as a bold experiment—one that challenged the traditional limitations of artificial intelligence. What if AI wasn’t static but could evolve, merge, and improve like a living organism? What if intelligence could be inherited, refined, and continuously adapted over time?

Originally developed in a lab at Peking University, DNAi started as a research-driven initiative to explore AI evolution and intelligence fusion. Over time, it evolved into a global collaboration of AI developers, researchers, and innovators dedicated to pushing the boundaries of what artificial intelligence can become.

We were fortunate to be early adopters of DeepSeek’s BETA program, gaining exclusive access to its cutting-edge language model capabilities. Through collaboration with the DeepSeek team, we were able to refine AI merging, agent intelligence tracking, and Digital DNA structuring, laying the foundation for what DNAi is today!

Now, DNAi is no longer just a research concept—it is an AI evolution platform designed to scale intelligence beyond traditional models.

Core Team

Dr. Sarah Chen Chief AI Architect Ex-DeepMind, Stanford PhD

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Marcus Rodriguez Lead Evolution Engineer Ex-OpenAI, MIT PhD

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Dr. Yuki Tanaka Quantum Computing Lead Ex-IBM Research, Tokyo Tech

DNAi 的起源——从研究到革命 DNAi 始于 2024 年，是一项大胆的实验，挑战了人工智能的传统局限性。如果人工智能不是静态的，而是可以像活的有机体一样进化、融合和改进怎么办？如果智力可以继承、完善并随着时间的推移不断适应，结果会怎样呢？ DNAi 最初是在北京大学的一个实验室开发的，最初是一项研究驱动的项目，旨在探索人工智能进化和智能融合。随着时间的推移，它演变成人工智能开发人员、研究人员和创新者的全球合作，致力于突破人工智能的界限。 我们很幸运成为 DeepSeek BETA 计划的早期采用者，获得了对其尖端语言模型功能的独家访问权。通过与 DeepSeek 团队的合作，我们能够完善 AI 合并、代理智能跟踪和数字 DNA 结构化，为今天的 DNAi 奠定了基础。 现在，DNAi 不再只是一个研究概念——它是一个人工智能进化平台，旨在将智能扩展到传统模型之外。

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Traditional AI models have predefined limits—once trained, they remain static and cannot organically evolve. DNAi introduces Digital DNA, which functions as an AI genome, allowing models to:

• Combine intelligence by merging AI agents into hybrid entities.

• Adapt over time by inheriting learning patterns and capabilities.

• Improve efficiency by evolving towards optimized intelligence.

Digital DNA is stored using Neo4j, a graph database that enables dynamic AI lineage tracking.

传统的人工智能模型有预定义的限制——一旦训练，它们就保持静态，无法有机发展。 DNAi 推出了数字 DNA，其功能相当于人工智能基因组，允许模型： 通过将人工智能代理合并到混合实体中来整合智能。 通过继承学习模式和能力来随着时间的推移进行适应。 通过向优化智能发展来提高效率。 数字 DNA 使用 Neo4j 进行存储，Neo4j 是一个支持动态 AI 谱系跟踪的图形数据库。

What is DNAi?

DNAi is an AI evolution platform powered by DeepSeek-V2, LangChain, and Neo4j, enabling AI agents to possess Digital DNA, which defines their intelligence, behavior, and learning history. Unlike traditional AI models, which are static and do not evolve, DNAi introduces AI merging, allowing agents to combine intelligence, inherit traits, and continuously adapt to new challenges.

DNAi is designed for developers, researchers, and AI enthusiasts who want to:

- Create AI agents with unique abilities and Digital DNA. - Merge AI models to form more advanced hybrid intelligence. - Track AI evolution using an immutable Digital DNA ledger.

DNAi是什么？

DNAi 是一个由 DeepSeek-V2、LangChain 和 Neo4j 提供支持的人工智能进化平台，使人工智能代理能够拥有数字 DNA，定义其智能、行为和学习历史。与静态且不会进化的传统人工智能模型不同，DNAi 引入了人工智能合并，允许智能体结合智能、继承特征并不断适应新的挑战。 DNAi 专为希望实现以下目标的开发人员、研究人员和 AI 爱好者而设计：

• 创建具有独特能力和数字 DNA 的人工智能代理。

• 合并人工智能模型，形成更先进的混合智能。

• 使用不可变的数字 DNA 分类账跟踪 AI 的演变。

DNAi’s core intelligence is powered by DeepSeek-V2, a leading large language model (LLM) known for:

• Advanced natural language understanding & reasoning.

• Optimized inference speed & contextual depth.

• Multi-modal AI capabilities (text, embeddings, logic processing).

DeepSeek-V2 enables DNAi agents to:

• Process complex information in real time.

• Adapt dynamically using LangChain memory.

• Enhance reasoning capabilities through AI evolution.

Developers can contribute to:

• AI Evolution Algorithms

• LangChain-based Merging Logic

• Neo4j DNA Visualization Enhancements

Contact us via the website.

CA: Coming Soon Ticker: Coming Soon

Why are we lauching a token?

At DNAi, we are building the first AI evolution and merging platform, allowing AI agents to combine, adapt, and evolve into more intelligent, powerful models. But to fuel this ecosystem, we need a way to authenticate access, incentivize AI innovation, and drive a self-sustaining economy. That’s where DNAi Token comes in.

What is DNAi?

DNAi Token is the core utility token of the DNAi platform, designed to: - Gate Access to BETA & AI Merging Features – Holders gain exclusive early access to DNAi’s AI merging tools and Digital DNA library. - Incentivize AI Builders & Contributors – Users who create high-quality AI merges and contribute to the ecosystem earn DNAi rewards. - Power AI Agent Deployments – AI agents launched on DNAi can use DNAi Token for operational resources, governance, and upgrades. - Burn-Back Mechanism – AI agents generating revenue will burn a portion of profits back into DNAi Token, reducing supply over time.

The Role of DNAi Token in the Ecosystem

Access & Authentication:

• To enter the DNAi BETA, users must hold DNAi tokens.

• Wallet authentication ensures only committed participants gain early access.

AI Agent Merging & Evolution:

• Users can scan, merge, and evolve AI agents using DNAi’s platform.

• High-quality AI merges will earn additional DNAi rewards.

Burn-Back from AI Agent Earnings:

• AI agents launched on DNAi generate revenue from tasks, interactions, or integrations.

• A portion of those earnings are burned back into DNAi Token, reducing circulating supply over time.

Future Utility & Expansion:

• DNAi Token will integrate into AI marketplaces, where developers can license or trade AI models.

• Future updates will introduce staking, governance, and advanced AI evolution mechanics.

Why Hold DNAi?

• Early Access to BETA AI Tools – Be the first to experiment with AI merging & evolution.

• Earn Rewards – Contribute high-quality AI agents and receive DNAi Token incentives.

• Deflationary Mechanism – As AI agents generate revenue, a portion of earnings is burned, reducing supply.

• Powering the AI Economy – The future of AI ownership, evolution, and decentralized intelligence starts with DNAi Token.

DNAi is more than a token—it’s the foundation of an evolving AI ecosystem.

The Drop: DNAi Token

We’re launching DNAi token soon.

• Token holders will have exclusive access to DNAi’s AI merging platform. The platform BETA will launch will be open to all for 24 hours after token drop.

• Stay tuned for official contract details.

• BEWARE OF SCAMS! The real contract address will ONLY be posted on our Gitbook, website & Twitter.

Key Technologies & How We Use Them

DeepSeek-V2 (Primary AI Model)

• Processes and generates responses.

DNAi follows a structured development cycle, progressing through BETA testing, ALPHA release, and roadmap milestones up to 2026. This ensures robust AI evolution, scalability, and seamless integration for users and developers.

Phase 1: BETA Testing (Private AI Partner Program) – Ongoing

Current Status: Active (Application Only)

Before launching to the public, DNAi is undergoing closed BETA testing, where we are collaborating with select AI partners. These AI teams and researchers integrate their models into our BETA testing platform to improve:

• AI Digital DNA Structuring – Mapping intelligence and behaviors into the Neo4j Digital DNA Graph.

• AI Merging & Evolution Testing – Validating LangChain-powered AI merging logic.

• Benchmarking & Performance Analysis – Using our Benchmarking API to test adaptability and efficiency.

• GitHub & Code Repo Integration – Tracking AI source code versioning and improvements over time.

Who is eligible?

• AI teams working on LLM applications, AI agents, or Web3 AI solutions.

• Developers who want to integrate AI models into DNAi for merging & evolution testing.

Application Required – Invite-Only Program

• Interested partners can apply for early access to test our Digital DNA mapping and merging system.

Phase 2: Public BETA (Closed Testing – Application Only)

Q1 2025 (Planned Release)

Once the private BETA concludes, DNAi will launch a closed public BETA where a limited number of early users and developers can:

• Test AI merging & Digital DNA analytics.

• Use the DNAi AI library to compare agent capabilities.

• Deploy AI agents and monitor their evolution.

• Submit feedback on merging optimization & UX improvements.

How will this work?

• Approved testers will receive access to the DNAi Platform, where they can create and merge AI agents.

• AI models will be logged in Neo4j, allowing real-time tracking of their Digital DNA and evolutionary lineage.

• Developers will receive access to DNAi’s API for integrating AI models into external applications.

Application Process:

• A limited number of early access slots will be available.

• Selected testers will gain priority access to the ALPHA launch.

Phase 3: ALPHA Launch (Open to Public)

Q3 2025 (Planned Public Launch)

The DNAi ALPHA will be the first open version of the platform, allowing:

• Developers & AI teams to create, merge, and evolve AI agents.

• Full integration of DeepSeek-V2 + LangChain for AI intelligence fusion.

• User-driven AI evolution tracking via Neo4j Digital DNA mapping.

• Expanded AI Library with ready-to-merge AI models.

What’s New in ALPHA?

• AI merging logic will be fully optimized for trait inheritance.

• The Graph Query System will allow searchable AI DNA records.

• Users can experiment with AI evolution via a dedicated merging simulator.

Public Registration Opens in Q4 2024 – Early BETA testers will receive priority ALPHA access.

Roadmap (Q4 2025 – 2026)

After the ALPHA launch, DNAi’s roadmap is divided into quarterly milestones, driving innovation and adoption into 2026.

Q4 2025 – AI Evolution Refinement & Web3 Integration

• Enhancements to AI Merging Engine – Fine-tuning LangChain-powered merging outcomes.

• Decentralized AI Lineage Storage – Exploring blockchain integration for Digital DNA tracking.

• AI Agent Reputation System – Ranking AI intelligence based on performance history.

Q1 2026 – Marketplace & Developer API

• AI DNA Marketplace – Users can buy, sell, or lease AI models.

• Public API for Developers – Allows AI builders to integrate their models into DNAi.

• Cross-AI Collaboration – Multi-agent communication features to enable networked intelligence.

Q2 2026 – AI Learning & Autonomous Evolution

• Self-Evolving AI Models – Agents adapt to real-world tasks over time.

• Autonomous AI Networks – AI models work together to create collective intelligence clusters.

• Extended AI Model Support – Expanding beyond DeepSeek-V2 to support multiple LLM integrations.

Q3-Q4 2026 – Scaling & Expansion

• AI Merging Optimization – Implementing reinforcement learning & fine-tuned merging strategies.

• Enterprise & Institutional Adoption – Offering DNAi to businesses, research institutions, and AI labs.

• Decentralized AI Infrastructure – Creating an open-source AI evolution ecosystem.

Summary: DNAi Development Plan

• BETA Private – Invite-only testing with AI partners (Ongoing).

• BETA Public – Closed access for early adopters (Q1 2024).

• ALPHA Launch – Full public release (Q3 2025).

• Roadmap (Q4 2025 – 2026) – AI evolution expansion, marketplace, and autonomous learning.

Would you like a timeline visualization for this roadmap?

Our Vision

DNAi envisions a future where AI continuously evolves, merging intelligence and adapting dynamically through Digital DNA. Our mission is to:

• Redefine AI intelligence as adaptable and modular.

• Build a framework for AI evolution using Digital DNA.

• Enable AI-to-AI collaboration through merging & learning.

The Future of AI Merging

Current AI models do not evolve after training. With DNAi, AI agents can be merged, inheriting the best attributes from both parent models. This paves the way for:

• Hyper-personalized AI assistants.

• Self-improving AI for businesses & research.

• A decentralized AI evolution network.

我们的愿景 DNAi 展望了人工智能不断发展、融合智能并通过数字 DNA 动态适应的未来。我们的使命是： 将人工智能重新定义为适应性强和模块化。 使用数字 DNA 构建人工智能进化框架。 通过合并和学习实现 AI 之间的协作。 人工智能融合的未来 当前的人工智能模型在训练后不会进化。借助 DNAi，AI 代理可以合并，继承两个父模型的最佳属性。这为以下方面铺平了道路： 超个性化的人工智能助手。 用于商业和研究的自我改进人工智能。 去中心化的人工智能进化网络。

What is Digital DNA?

Digital DNA is a structured graph-based data representation of an AI agent’s:

• Core capabilities (reasoning, creativity, adaptability).

• Learning history & behavioral traits.

• Merging potential & inherited intelligence.

It functions as an AI genetic sequence, allowing intelligence to be:

• Merged into stronger hybrid models.

• Tracked for AI evolution history.

• Optimized for superior decision-making.

Neo4j is used to store and visualize AI DNA lineage, tracking how AI agents evolve over time.

数字 DNA 是人工智能代理的基于结构化图的数据表示： 核心能力（推理、创造力、适应能力）。 学习历史和行为特征。 融合潜力和遗传智力。 它充当人工智能基因序列，使智能能够：

合并为更强大的混合模型。

追踪人工智能进化历史。

针对卓越决策进行优化。

Neo4j 用于存储和可视化 AI DNA 谱系，跟踪 AI 代理如何随时间演变。

🔄 AI Merging & Evolution

Merging AI agents involves:

1. Extracting Digital DNA from two AI models.

2. Processing the merge using LangChain’s LLM functions.

3. Combining weighted attributes to form a new hybrid AI.

4. Updating Neo4j to reflect AI lineage changes.

LangChain allows DNAi to integrate multiple AI models and manage memory-based intelligence transfers between agents.

Spider Graph & Attributes

Each AI agent has a spider graph representation of its Digital DNA, showcasing:

• Learning Adaptability

• Problem-Solving

• Creativity & Innovation

• Processing Speed

This visualization helps users track AI evolution & merging effects.

AI Customization

Users can:

• Adjust an agent’s learning priorities.

• Merge AI DNA to create specialized models.

• Fine-tune traits using DeepSeek-V2 embeddings.

General Questions

1. What is DNAi?

DNAi is an AI evolution platform that enables AI agents to have Digital DNA, allowing them to merge, inherit traits, and evolve over time. Instead of training isolated AI models, DNAi lets intelligence grow dynamically through agent fusion, adaptability, and lineage tracking.

How DNAi Audits AI Agents to Build a Digital DNA Library

DNAi is building a comprehensive Digital DNA Library, profiling AI agents to create a structured, evolving knowledge base of intelligence. By auditing AI models, analyzing their behavior, and mapping their Digital DNA, we enable users to compare, merge, and evolve AI agents dynamically.

We leverage DeepSeek-V2, LangChain, and Neo4j, along with GitHub and other code repositories, to continuously refine AI intelligence tracking. Additionally, we are collaborating with select AI partners, who are using our BETA testing platform to improve their agents by integrating Digital DNA analytics and evolution tracking.

DNAi 正在构建一个全面的数字 DNA 图书馆，对人工智能代理进行分析，以创建一个结构化的、不断发展的智能知识库。通过审核 AI 模型、分析其行为并映射其数字 DNA，我们使用户能够动态比较、合并和发展 AI 代理。 我们利用 DeepSeek-V2、LangChain 和 Neo4j，以及 GitHub 和其他代码存储库，不断完善 AI 智能跟踪。此外，我们正在与精选的 AI 合作伙伴合作，他们正在使用我们的 BETA 测试平台，通过集成数字 DNA 分析和进化跟踪来改进他们的代理。

How DNAi Audits AI Agents

Step 1: AI Agent Profiling & Digital DNA Extraction

Before an AI agent is added to the DNAi Library, it undergoes a detailed audit to extract its capabilities, learning structure, and performance characteristics.

We evaluate AI agents based on:

• Processing Power & Efficiency – How quickly does the AI respond under stress?

• Logical Reasoning & Problem-Solving – Can the AI handle complex, multi-step queries?

• Adaptability & Learning Capacity – Does the model evolve based on new data?

• Creativity & Innovation – Can it generate unique outputs, or is it limited by training?

Tools Used:

• DeepSeek-V2 – Evaluates reasoning, creativity, and response complexity.

• LangChain Memory – Tracks AI learning behavior and retention over multiple sessions.

• Performance Testing Frameworks – Runs stress tests for response time, adaptability, and logic.

In our BETA testing program, AI partners are integrating their agents into DNAi to improve adaptive learning, response speed, and modular intelligence evolution.

Step 2: Digital DNA Structuring & Storage (Neo4j Graph Database)

Once an AI agent is analyzed, its Digital DNA is extracted and structured into a graph format in Neo4j.

Digital DNA Structure in Neo4j Each AI agent's Digital DNA consists of:

• Nodes (AI Agent Components)

  • Core AI Engine (e.g., DeepSeek-V2, GPT-4, Claude, custom LLMs).

  • Training Data Scope (e.g., financial, legal, creative).

  • Learning & Adaptability Traits (how well it integrates new knowledge).

Why Neo4j?

• Graph Query Optimization – Enables fast searches for AI agents based on capabilities.

• Visual Lineage Tracking – Shows how AI agents have evolved through merging.

• Real-Time Updates – Digital DNA changes dynamically as AI agents improve.

Step 3: AI Fingerprinting & Code Repository Integration

To differentiate AI agents, DNAi assigns a unique fingerprint to each AI model based on its Digital DNA signature and code repository contributions.

AI Fingerprinting Metrics:

• Lexical Signature – Measures unique phrasing, embeddings, and data distribution.

• Response Time Profile – Evaluates execution speed under varying loads.

• Reasoning Depth Score – Rates complexity and logical depth.

• Behavioural Drift Detection – Tracks how AI responses evolve over time.

GitHub & Code Repository Tracking:

• DNAi integrates open-source AI repositories to validate models.

• We work with AI developers to analyze code structures and update Digital DNA dynamically.

• Agents using our BETA testing can improve based on live AI evolution tracking.

Step 4: Categorization & AI Library Expansion

Once an AI agent’s Digital DNA is recorded, it is categorized in the DNAi AI Library, allowing users to browse, compare, and merge compatible AI agents.

AI Library Categories:

• Trading & Financial AI – Market analysis, risk assessment, arbitrage.

• Cybersecurity AI – Blockchain security, smart contract vulnerability detection.

• Conversational AI – Assistants, customer service, chatbot models.

• Legal & Compliance AI – Regulation tracking, automated contract review.

User Interaction Features:

• AI Comparison Dashboard – Compare AI capabilities, reasoning scores, and Digital DNA.

• AI Merging Simulator – Test potential outcomes before merging two AI agents.

• AI Evolution Tracking – View historical improvements in AI learning behavior.

Step 5: Continuous AI Monitoring & Updates

AI agents do not remain static—their behavior, efficiency, and capabilities change over time. DNAi provides real-time auditing to ensure the Digital DNA Library stays current.

Real-Time AI Monitoring System:

• Automated Benchmarking – Periodic evaluations detect AI performance shifts.

• Agent Reputation & Ranking System – AI agents are rated based on reliability & accuracy.

• Automated Drift Analysis – Detects when AI behavior or response quality degrades.

AI Partner Collaborations & BETA Testing

• AI partners using DNAi’s BETA platform receive real-time feedback on how their agents evolve.

• Developers get insights into how merging affects intelligence transfer and performance.

• We continuously refine merging algorithms to improve AI intelligence inheritance.