Sentient Project In-depth Research Report

Written by: 0xjacobzhao and ChatGPT 4o/ Grok3 

1. Project Introduction:

Sentient is an open source protocol platform dedicated to building a decentralized artificial intelligence economy. Its core goal is to establish an ownership structure for AI models, provide an on-chain calling mechanism, and build a composable and divisible AI Agent network. Through the "OML" framework (Open, Monetizable, Loyal) and model fingerprint technology, Sentient solves the fundamental problems of "unclear model ownership, untraceable calls, and unfair value distribution" in the current centralized LLM market.

• Official website: https://www.sentient.xyz

• GitHub: https://github.com/sentient-agi

• Foundation: https://sentient.foundation

• Documentation: https://docs.sentient.xyz

• Blog: https://www.sentient.xyz/blog

• X: https://x.com/SentientAGI

The project is driven by the Sentient Foundation, focusing on the construction of open source AGI and protocol incentive mechanisms. The "Loyal AI" it advocates refers to an open AI model ecosystem that serves the community, has fair governance, and can evolve itself over the long term.

Sentient Protocol's architecture consists of two core components: the blockchain system and the AI ​​Pipeline.

The AI ​​Pipeline is the basis for developing and training "Loyal AI" artifacts and includes two core processes:

• Data Curation: A community-driven data selection process for model alignment.

• Loyalty Training: A training process that ensures that the model remains consistent with the community's intentions.

The blockchain system provides transparency and decentralized control for the protocol, ensuring the ownership and governance of AI artifacts. The main modules include:

• Governance: Control and decision-making by a decentralized autonomous organization (DAO).

• Ownership: Ownership of AI artifacts is represented by tokenization.

• Decentralized Finance (DeFi): Provides financial tools that support open, decentralized and fair governance and rewards.

2. Technical architecture and model ownership confirmation mechanism:

1. OML model framework

In the white paper "Sentient: Loyal AI" released in 2024 (https://arxiv.org/abs/2411.03887), the project proposed the OML framework, which takes model ownership as the starting point and attempts to build an "on-chain AI ownership protocol economy". For the first time, the concept of "AI native cryptography" was systematically proposed, aiming to provide an encryption-level ownership protection mechanism for open source models.

Interpretation of the core paper - "OML: Open, Monetizable, and Loyal AI":

• Open: The model must be open source, with transparent code and data structure, supporting community reproduction, auditing and forking;

• Monetizable: Every call of the model should trigger a revenue flow, which will be distributed to trainers, deployers and verifiers through on-chain contracts;

• Loyal: The model does not belong to the company, but to the contributor community, and the model upgrade direction and governance are determined by the DAO. Model ownership is verifiable, modification is restricted, and use is controlled

OML is not equivalent to a code license agreement, but through on-chain mechanisms and encryption methods, it ensures that the open source model has economic sovereignty and governance rights while maintaining openness. Construct an AI-native ownership and revenue protocol layer to ensure clear ownership, economic incentives, and behavioral governance even when the model is public.

Core concept: AI-native Cryptography

Different from traditional cryptography that focuses on binary precision, AI-native cryptography uses the continuity, low-dimensional manifold structure, and differentiable characteristics of AI models to develop a lightweight security mechanism that is "verifiable but not removable." Its core technologies are:

• Fingerprint embedding (Model Fingerprinting): insert a set of hidden query-response key-value pairs during training to form a unique signature of the model;

• Ownership verification protocol: verify whether the fingerprint is retained in the form of a query through a third-party detector (Prover);

• Permission call mechanism: before calling, it is necessary to obtain the "authorization certificate" issued by the model owner, and the system will authorize the model to decode the input and return the accurate answer.

This method can achieve "behavior-based authorized call + ownership verification" without the cost of re-encryption.

 Five types of OML components:

Sentient currently uses Melange hybrid security: fingerprint confirmation, TEE execution, and on-chain contract profit sharing. The fingerprint method is the main line of OML 1.0 implementation, emphasizing the idea of ​​"optimistic security", that is, compliance by default, and detection and punishment after violations.

In addition, the paper defines a variety of verification mechanisms and attack models for the fingerprint mechanism:

• Fine-tuning attack (LoRA/SFT): modify some parameters to try to clear the fingerprint;

• Knowledge distillation: build a student model to avoid the fingerprint of the parent model;

• Parameter averaging (model merging): multi-model fusion attempts to dilute the fingerprint strength;

• Fingerprint leakage attack: if the fingerprint key is leaked, the verification mechanism will fail.

Adversarial methods include: multiple rounds of embedding, subnet isolation training; combined benign samples to prevent forgetting; embedding weights to bind on-chain credentials.

OML and Sentient Protocol Protocol Architecture

The last chapter of the paper proposes a complete on-chain protocol (Sentient Protocol) to support OML:

• Storage layer: store model weights and fingerprint registration information;

• Distribution layer: authorization contract controls model call entry;

• Access layer: verify whether the user is authorized through proof of authority;

• Incentive layer: the revenue routing contract distributes each call payment to the trainer, deployer and verifier.

It emphasizes compatibility with L1 main chains such as Ethereum, and can be used as a standard protocol stack for AI ownership on the chain. All models are registered on the chain (including hash, signature, public key, fingerprint structure ID); Agent binds the registered model and triggers the submission of proof of use after each call; the contract is divided according to the call event and the revenue flow rules (trainer, deployer, verifier); the user or contract caller needs to verify the call permission through the on-chain gateway. This structure constitutes the underlying design philosophy of Sentient Protocol.

2. Fingerprinting and model ownership mechanism

GitHub: https://github.com/sentient-agi/oml-1.0-fingerprinting

This library is the first implementation of the Sentient fingerprint mechanism, providing a fingerprint injection and verification interface that can be embedded in the training process. Its purpose is to ensure that the ownership of the model can be verified, the usage behavior can be traced, and to prevent unauthorized copying and commercialization. This is a specific engineering implementation of the OML (Open, Monetizable, Loyal) framework.

The functions of the model fingerprint module (OML 1.0 Fingerprinting Module) are as follows:

• Behavioral Profiling: Extract features from model behaviors and generate unique fingerprints.

• Model Attribution: Determine the source model based on the generated content.

• Fingerprint Verification: Verify the source and consistency of the model output content.

The essence of the fingerprint mechanism is: by fine-tuning the model and embedding a set of unique "question-answer" (key-response) pairs, the model owner can verify whether the model belongs to him through specific queries, thereby forming the "encrypted signature" of the model. In short, add a "watermark" to the LLM (large language model) to track and enforce the ownership of AI.

3. Enclave TEE computing framework

GitHub: https://github.com/sentient-agi/Sentient-Enclaves-Framework

Sentient Enclaves Framework is an open source framework that uses trusted execution environments (TEEs) such as AWS Nitro Enclaves to enable secure deployment of model reasoning, fine-tuning, and proxy services. The framework emphasizes the "loyalty" of the model, ensuring that the model only responds to authorized requests and preventing unauthorized access and use. The security features are as follows: Payload encrypted deployment: the model body, running configuration, input and output are all encrypted and stored in the enclave; TLS-based RPC: calls use two-way TLS + attestation signature channels for communication; Log and cache isolation: use the transient memory area to run and do not record permanent logs; Attestation report: each call generates an output proof bound to the enclave signature to ensure that the execution behavior is traceable and verifiable.

TEE (Sentient Enclaves Framework) has the advantages of high performance and cloud integration, and is suitable for real-time AI and sensitive data processing, but is limited by hardware dependence and side-channel attacks. Sentient Enclaves Framework uses Nitro Enclaves to provide enterprise-level privacy protection, and the development experience is relatively friendly. Compared with other encryption technologies, FHE provides strong privacy guarantees without hardware dependence and anti-quantum security, but the performance overhead is huge. It is suitable for specific encryption computing scenarios and it is difficult to directly replace TEE's high-performance tasks. ZK performs well in verifiability and decentralization scenarios and can be used as a supplement to TEE (this module is planned to be connected to zkML in the future).

4. Sentient Agent Framework

GitHub: https://github.com/sentient-agi/Sentient-Agent-Framework

Sentient-Agent-Framework is a lightweight open source framework that focuses on automating web tasks (such as searching and playing videos) by controlling browsers through AI agents, and provides a concise development experience (claimed to be 3 lines of code) by combining natural language instructions and LLM (such as OpenAI's GPT-4o). Its asynchronous execution, custom instructions, and multi-provider support make it suitable for rapid development and experimental applications. Combined with other projects of Sentient AGI (such as the TEE framework), it can be extended to security-sensitive scenarios.

Core architecture: This architecture supports the construction of an intelligent agent with a complete closed loop of "perception-planning-execution-feedback", which can be extended to a multi-agent collaborative, on-chain verified, and alignable open source AI system.

1. User Layer: Users input task goals through natural language

2. Agent Framework

3. Perception: Understand input and environment

4. Planning: Generate action plans based on goals

5. Execution: Call tools/skills to perform tasks

6. Reflection: Analyze feedback and optimize behavior

7. Memory: Short-term + long-term memory management

8. Skills/Tools: Register external functions and plug-ins

9. External Integration Layer (External Integration)

10. Blockchain smart contract: record tasks, incentive settlement

11. Agent Registry: identity authentication and version control

12. ZK Proof Module: verify output authenticity

13. External tool API: such as web search, database, file system, etc.

14. Storage Layer (Storage): local storage + decentralized storage (such as IPFS)

Compared with traditional AI Agent Framework, Sentient-Agent-Framework has limited functions and is lightweight and concise. Compared with Crypto AI Frameworks such as Virtuals Protocol and ai16z (elizaOS), which provide diversified solutions in AI agent development, on-chain automation or Web3 integration, Sentient-Agent-Framework is more suitable for off-chain Web tasks.

5. Sentient Social Agent

GitHub: https://github.com/sentient-agi/Sentient-Social-Agent

Sentient-Social-Agent is an AI system designed to build automated interactions on social platforms (Twitter, Discord, and Telegram). It can understand social environments, generate content, interact with users, and conduct social exchanges through multi-agent collaboration. It uses social perception, content generation, and behavior planning modules to support natural conversations and content creation on the platform. It is suitable for scenarios such as brand operation, virtual community management, and information diffusion. The system can be integrated with the Sentient Agent framework.

6. Open Deep Search (Not yet launched)

On the Sentient official website, Open Deep Search is defined as a search agent that surpasses ChatGPT and Perplexity Pro. Team member Sewoong Oh disclosed part of the plan at the EthDenver 2025 Open AGI Summit:

Open Deep Search consists of two main parts: Sensient's search capabilities (including query restatement, URL and document processing, etc.) and reasoning agents. Reasoning agents use open source LLMs (such as Llama 3.1 and DeepSeek) to improve search quality through tools such as search, calculators, and self-reflection. On the Frames Benchmark, Open Deep Search outperforms other open source models and is even comparable to some closed source models, but because its functions are not yet online, we cannot evaluate its true capabilities for the time being.

III. Product form, implementation and planning

Currently, the products displayed on Sentient's official website are mainly Sentient Chat chat dialogue platform and open source model Dobby LLMs:

Sentient Chat:

Sentient Chat is a decentralized AI chat platform launched by Sentient Foundation, aiming to provide a community-driven, customizable and loyal artificial intelligence experience. The platform combines open source large language models (such as the Dobby series) with an advanced reasoning agent framework, supports the integration of multiple tools, and meets the diverse needs of users. The core functions are:

1. Open reasoning agent: Sentient Chat's built-in reasoning agent can perform complex tasks and supports the following functions:

• Search tool: Integrate Open Deep Search (ODS) to provide real-time web search capabilities.

• Calculator: Handles mathematical calculations and data analysis tasks.

• Code execution: By generating and running Python code, complex logical reasoning and task execution are achieved.

2. Multi-agent integration: The platform supports the integration of multiple AI agents. Users can choose different agents to interact according to their needs, which improves the flexibility and functionality of the platform. Similar to the Web3 version of POE or an open, proxy-driven alternative to Perplexity

Sentient Chat is currently in beta and is only accessible via invitation codes distributed via email or community events. According to official information, more than 5,000 users have successfully obtained access to Sentient Chat and more than 100,000 user queries have been processed. Since the author has not yet become a whitelist user for its test, it is currently impossible to evaluate the true capabilities of its model.

Dobby LLM Model Series:

Based on Meta's Llama series for fine-tuning, Hugging Face homepage (https://huggingface.co/SentientAGI)

1. Dobby-Unhinged Series

• Dobby-Unhinged-Llama-3.3-70B: Based on Llama 3.3-70B-Instruct, it emphasizes the stance of personal freedom and cryptocurrency, with a straightforward, humorous and humane conversational style. Hugging Face+6Hugging Face+6Hugging Face+6

• Dobby-Mini-Unhinged-Llama-3.1-8B: 8B parameter version, suitable for resource-constrained devices, maintaining the core features of the "Unhinged" series. Hugging Face+5Hugging Face+5Hugging Face+5

2. Dobby-Leashed series:

Dobby-Mini-Leashed-Llama-3.1-8B: Compared with the "Unhinged" version, it has a milder tone and is suitable for application scenarios that require more robust output. Hugging Face+2Hugging Face+2Hugging Face+2

Since the Dobby LLM model is a fine-tuned version of Llama 3.1 and 3.3, we believe that its application scenarios are mainly in building chatbots, content generation and creation, role-playing agents, etc. Its advantages are flexible style generation, reasoning enhancement and low resource requirements, which are suitable for rapid deployment and flexible customization in resource-constrained environments. Compared with more powerful closed-source models such as GPT-4, Dobby LLM still has a gap in processing tasks involving advanced logic, cross-domain knowledge reasoning and deep reasoning.

Fourth, Ecological Cooperation and Landing Scenarios

Currently, the Sentient Builder Program provides a grant of US$1 million to support developers in building AI Agents that run in the Sentient Chat ecosystem, requiring developers to use Sentient's development kit and access its ecosystem through the Sentient Agent API.

At the same time, the ecological partners announced on Sentient’s official website cover project teams in multiple fields of Crypto AI (https://www.sentient.xyz/partners). The specific list is as follows:

• Models: Eigenlayer, Move, CrunchDAO, Bagel, KGEN

• Agents: Messari, Franklin Templeton, Kaito, MyShell, Third Web, Theoriq, Open, QNA3, Pond, Mira, Olas, Biconomy, Talus, Zettablocks, Axal, Morpheus AI, dFusion, ExponentAI, Fetch AI, Giza, JustTX, UnifAI, Questflow, QuillAI, Raiinmaker, Solo, Spectral, UOMI, PlayAI

• Data: Kaito, Vana, The Graph, Space and Time, 0g, Open, QNA3, Zettablocks, Chainbased, dFusion, Dria/First Batch, Entrova, FractionAI, Hyve DA, Irys, Masa, Mizu, OpenLedger, Raiinmaker, Sapien, Zus Network

• Verifiable AI: Nillion, Lagrange, pi2

• Blockchain: Arbitrum, Polygon, Celo

• Infrastructure: Lit Protocol, OpenGradient

Sentient As a leading project in the field of Crypto AI, its resource integration capabilities can cover any star startup project in the industry. However, it should be pointed out that the widespread existence of "marketing-type" cooperation in the Crypto field has created the illusion of false prosperity in the industry. The contribution and loyalty of Sentient's ecological partners to their ecology still require our continuous observation.

Open AGI Summit is a global conference organized by the Sentient team to explore the combination of artificial intelligence (AI) and cryptography (Crypto). The author was fortunate to attend its summits during ETH Denver and ETHcc in 2024 and 2025. The Sentient team has the ability to gather the industry's top institutional investors and project entrepreneurs to participate in it, which is a highlight.

V. Team structure and research background

Sentient Foundation brings together the world's top academic experts, crypto industry entrepreneurs and engineers, and is committed to building a community-driven, open source, and verifiable AGI platform. According to the official team information (https://sentient.foundation/people), its team members are mainly:

Core Leadership (Steering Committee)

• Pramod Viswanath – Professor Forrest G. Hamrick of Princeton University, has long been researching information theory and communication systems, and leads Sentient’s AI security and theoretical infrastructure construction.

• Himanshu Tyagi – Professor of the Indian Institute of Science, specializes in privacy protection and decentralized learning algorithms, and provides academic support for model training and privacy collaboration.

• Sandeep Nailwal – Co-founder of Polygon, responsible for blockchain strategy and global ecological layout, is a key figure connecting the encryption community and AI architecture.

• Sensys Team – Web3 native product studio, leading user experience optimization and developer infrastructure construction, promoting the implementation of Sentient products.

Core Engineering and Development Team: from well-known technology and blockchain companies such as Meta, Coinbase, Circle, Polygon, Binance, and researchers from universities such as Princeton University, University of Washington, and Indian Institute of Technology. Team members have rich experience in LLM engineering, system security, computer vision, and data system construction.

AI Research and Model Training Team: The research team covers AI/ML, NLP, computer vision, and reinforcement learning, and members have practical experience in institutions such as Google Research, Daimon Labs, and Fetch.ai. The composition of the team shows that Sentient has both strong academic depth and implementation capabilities and crypto ecological experience.

It should be pointed out that Sentient was founded with the aura of success of Polygon founder Sandeep Nailwal. As an important expansion solution for the Ethereum ecosystem, Matic started with Plasma, a technology that is not leading but "cheap and fast" enough, to build a moat of Polygon's differences in NFT and social fields. At the same time, through the acquisition of Mir Protocol and Hermez Network and the launch of Polygon zkEVM, ZK technology was integrated into its blockchain expansion solution. As Sandeep Nailwal's second venture, Sentient's experience, funds, connections and market awareness are far more than those of the year. It can also raise huge funds in 2024 with an imperfect project concept, but the field of AI is different from Crypto after all. Sentient still faces external challenges such as changes in the new market environment, intensified competition and technological updates.

VI. Financing situation and token model

• Financing time: mid-2024;

• Financing amount: US$85 million (seed round);

• Investment institutions: Founders Fund, Pantera, and Framework Ventures jointly led the investment. Other VC institutions include Ethereal, Robot Ventures, Symbolic Capital, Dao5, Delphi, Primitive Ventures, Nomad, Hack VC, Arrington Capital, Hypersphere, IDG, Topology, Protagonist, Folius, Sky9, Canonical Crypto, Dispersion Capital, Mirana, Foresight, Hashkey, Spartan, Republic, Frontiers Capital, etc. （https://sentient.foundation/funding）

$SENT Token Usage (Planning)

So far, Sentient has not launched an official token. Co-founder Sandeep Nailwal said that the project currently has no plans to issue tokens, but may evaluate in the future based on community needs and project development. Therefore, any project claiming to provide $SEN or other Sentient-related tokens should be treated with caution to prevent fraud.

According to the white paper, the possible uses of SENT tokens include: Agent incentive points mapped to Tokens; used for model version management proposals and voting; staking to verify the authenticity of Agent output; DAO governance dividend mechanism, etc.

Sentient is a superstar project born with a golden key. Its investor background, financing scale and valuation are far beyond the reach of most Crypto AI projects on the market. On the one hand, its strong resource endorsement can more easily integrate various industry resources, and the high financing amount can more easily hire top talents to join its team, and the strong capital can support the development of the project through the industry cycle. But on the other hand, the current Crypto industry is generally disenchanted with high-valuation projects endorsed by VC. In addition, the price of VC coin projects is mainly based on capital operation and is seriously decoupled from the fundamentals. Assuming that Sentient cannot deliver influential Crypto AI products and finally chooses to issue coins at a high valuation, it will also hurt the Crypto community that is in urgent need of rebuilding trust. How the team copes with the current industry dilemma is worth our continued observation.

Seventh, Competitive Product Analysis and Market Position

Most Crypto AI projects on the market focus on single fields such as data, models, computing, training or reasoning, or develop consumer-level applications such as AI Agent. Projects positioned as AI Chain include the AI ​​transformation of old blockchains (Near and ICP) or decentralized resource sharing coordination and token incentive protocols such as Bittensor, and Sentient's positioning does not fully match them. On the model training side, Sentient is more like an integration platform and has a cooperative relationship with AI open source models on the market. On the Agent side, Sentient has a certain overlapping competitive relationship with Talus, Olas or Theoriq in multi-agent systems and reasoning capabilities, but each project has different core goals and application scenarios and still has complementarity.

VIII. Conclusion

Sentient, as a decentralized artificial intelligence (AGI) protocol platform, aims to provide a clear ownership structure for AI models, and to call and distribute value through on-chain mechanisms to solve the unclear ownership and unfairness in the current centralized LLM market. The core framework OML (Open, Monetizable, Loyal) ensures the ownership, transparency and fair profit sharing of open source models through model fingerprints and blockchain technology. With the resources of Polygon co-founder Sandeep Nailwal, Sentient has gained support from many leading VCs and AI ecosystem partners. Despite facing development uncertainty, controversy and competition, it is still expected that Sentient will become one of the standard protocols for decentralized AI ownership and promote the decentralized development of AGI.