Author: Fairy, ChainCatcher

In today's rapidly evolving landscape of blockchain and artificial intelligence technologies, effectively combining the two has become one of the goals of many innovative projects. Ambient has emerged in this context, dedicated to integrating decentralized blockchain architecture with large-scale AI inference, exploring a new model of intelligent economy.

As a complete fork of Solana, Ambient retains Solana's speed and efficiency while introducing the Logits Proof (PoL) mechanism, creating a brand new blockchain ecosystem.

What is Ambient?

Ambient is a Layer-1 blockchain that combines Solana SVM compatibility with an innovative proof-of-work mechanism, providing large-scale verification inference. The core idea of the Ambient project is to deeply integrate AI inference and blockchain, creating a decentralized AI economy.

Unlike traditional proof-of-stake (PoS) systems, Ambient adopts an incentive mechanism similar to Bitcoin, providing predictable profits for each node participating in network inference, fine-tuning, or training. This approach avoids reliance on enterprise-grade GPUs and ensures sustainable profitability for miners through compensation based on transactions and inflation. Both miners and users can receive rewards that match their contributions, while the platform's value continues to grow with the network.

Features of Ambient:

• Efficient inference and security: Provides fully verified inference with overhead below 1%, while ensuring high security on large intelligent models (600B+ parameters) and their fine-tuned versions.
• Outstanding training performance: Training performance is improved by 10 times compared to existing methods, enhancing the training efficiency of AI models.
• High miner utilization: By optimizing on a single model, miner utilization is increased, enhancing the efficiency of the inference and verification processes.
• Non-blocking proof-of-work consensus: Adopts a non-blocking proof-of-work mechanism, ensuring economic competition in core network activities (inference, fine-tuning, training) while maintaining high TPS, avoiding performance bottlenecks of traditional blockchains.

Background and Development Status of the Ambient Team

Apart from the founder's background, Ambient has not disclosed information about other members. Ambient's CEO and founder, Travis Good, has a diverse academic background covering government studies, economics, computer science, and machine learning. Travis's leadership style emphasizes execution and pragmatism, consistently focusing on practical operations and executable solutions while driving technological innovation. Additionally, Travis is very active on Twitter, often sharing his unique insights on technology, innovation, and industry trends.

On April 1, Ambient completed a $7.2 million seed round of financing, led by a16z CSX, Delphi Digital, and Amber Group. Other participants include Big Brain Holdings, Superscrypt, Proof Group, Rubik Ventures, Aethir Foundation, and Edessa Capital. Ambient plans to launch its testnet in the second or third quarter.

Logits Proof Consensus Mechanism

The "Logits Proof" algorithm leverages a key fact: logits (which can be understood as logical units) serve as unique fingerprints and can effectively capture the model's "thinking" state (i.e., the model's "streaming" output) at a specific moment through the hash values generated during the model generation process. Under this mechanism, the hash value of the logits proof is a hash list of the hash values of each group of logits before each output token. In short, for each token n, up to the final token t, the hash value of the logits proof is:

Hash(Hash(n) … Hash(t))

The hash value of the logits progress marker proof is the logits hash after generating x tokens, where x is between n and t (inclusive of n and t), that is:

Hash(n) … Hash(x) … Hash(t)

Based on this principle, a verification mechanism can be constructed: first, the miner generates text; then, the verifier randomly selects a word from the text and requests the miner to provide the "thinking state" at that point (i.e., the corresponding logits progress marker proof hash). The verifier then performs inference on that word in the same model and context, generating their own "thinking state." If the two "thinking states" (represented by hash values) match, the verification is successful.

This proof-of-work mechanism aligns with the design principles of Bitcoin: mining (in this case, repeatedly executing the model through inference of 4000 tokens) is costly, but the verification process is very inexpensive (requiring only 1 token's inference). This mechanism not only improves efficiency but also ensures the security and reliability of the verification.

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