環境利用Solana的高速和效率，引入了邏輯證明（POL）機制

隨著區塊鍊和人工智能技術的發展，如何有效地結合兩者已成為目標之一

As blockchain and artificial intelligence technologies continue to develop, how to effectively combine the two has become one of the goals of many innovative projects. Ambient was born in this context, dedicated to combining decentralized blockchain architecture with ultra-large-scale AI reasoning to explore a new intelligent economic model.

隨著區塊鍊和人工智能技術的不斷發展，如何有效地結合兩者已成為許多創新項目的目標之一。環境是在這種情況下誕生的，致力於將分散的區塊鏈體系結構與超大規模的AI推理相結合，以探索一種新的智能經濟模型。

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

作為一個完整的Solana叉，環境保留了Solana的高速和效率，並通過引入邏輯（POL）機制來創建一個全新的區塊鏈生態系統。

What is Ambient?

什麼是環境？

Ambient is a Layer-1 blockchain that combines Solana SVM compatibility with a novel proof-of-work mechanism to provide ultra-large-scale verified reasoning. The core concept of the Ambient project is to deeply integrate AI reasoning and blockchain to create a decentralized AI economy.

環境是一個1層區塊鏈，將Solana SVM兼容性與新穎的工作證明機制相結合，以提供超大規模的驗證推理。環境項目的核心概念是深入整合AI推理和區塊鏈，以創建分散的AI經濟。

Unlike traditional proof-of-stake (PoS) systems, Ambient uses an incentive mechanism similar to Bitcoin and provides predictable profits for each node that participates in network reasoning, fine-tuning, or training. This approach avoids reliance on enterprise-level GPUs and ensures sustainable profitability for miners through transaction- and inflation-based compensation. Both miners and users can receive rewards that match their contributions, and the value of the platform continues to increase as the network grows.

與傳統的證明（POS）系統不同，環境使用類似於比特幣的激勵機制，並為參與網絡推理，微調或培訓的每個節點提供可預測的利潤。這種方法避免依賴企業級的GPU，並通過基於交易和通貨膨脹的薪酬確保礦工的可持續盈利能力。礦工和用戶都可以收到與其貢獻相匹配的獎勵，並且該平台的價值隨著網絡的增長而繼續增加。

Features of Ambient:

環境的特徵：

Ambient team background and development status

環境團隊的背景和發展狀況

In addition to the background of the founder, Ambient has not yet disclosed information about other members. Ambient's CEO and founder Travis Good has a diverse academic background, and his academic career covers four fields: government, economics, computer science, and machine learning. Travis' leadership style focuses on execution and pragmatism. When promoting technological innovation, he always adheres to practical operations and focuses on executable solutions. In addition, Travis is very active on Twitter and often shares his unique insights on technology, innovation, and industry trends.

除了創始人的背景外，Ambient尚未披露有關其他成員的信息。 Ambient的首席執行官兼創始人Travis Good具有多樣化的學術背景，他的學術生涯涵蓋了四個領域：政府，經濟學，計算機科學和機器學習。特拉維斯的領導風格著重於執行和實用主義。在促進技術創新時，他始終遵守實踐運營，並專注於可執行的解決方案。此外，特拉維斯（Travis）在Twitter上非常活躍，並且經常分享他對技術，創新和行業趨勢的獨特見解。

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

4月1日，Ambient完成了由A16Z CSX，Delphi Digital和Amber Group領導的720萬美元種子融資。大腦持有，SupersCrypt，Proof Group，Rubik Ventures，Aethir Foundates，Edessa Capital等。環境計劃在第二/第三季度啟動測試網。

Logits Proof of Consensus Mechanism

共識機制的邏輯證明

The "Logits Proof" algorithm takes advantage of a key fact: logits (which can be understood as logical units) are both unique fingerprints and can effectively capture the "thinking" state of the model at a specific moment (that is, when the model is "streaming" output) through the hash value generated during the model generation process. Under this mechanism, the hash value of the logits proof is a hash list of each set of logits hash values before each output token. In short, for each token n, until the final token t, the hash value of the logits proof is:

“ logits證明”算法利用了一個關鍵事實：logits（可以理解為邏輯單元）既是獨特的指紋，又可以在特定時刻（即，當模型是“流”輸出“流”輸出）通過模型生成過程中的哈希值有效地捕獲模型的“思維”狀態。在此機制下，邏輯證明的哈希值是每個輸出令牌之前的每組邏輯哈希值的哈希列表。簡而言之，對於每個令牌n，直到最終令牌t為止，logits證明的哈希值是：

Hash(Hash(n) … Hash(t))

哈希（哈希（n）…哈希（t））

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

logits進度標記的哈希值是生成x令牌後的logitts哈希，其中x在n和t之間（包含在內），也就是說：

Hash(n) … Hash(x) … Hash(t)

哈希（n）…哈希（x）…哈希（t）

Based on this principle, a verification mechanism can be constructed: first, the miner generates text; then, the verifier randomly selects a word in the text and asks the miner to provide the "mental state" at that point (that is, the corresponding logits progress mark proof hash). Then, the verifier performs a reasoning on the word in the same model and context to generate its own "mental state". If the "mental states" of the two (represented by hash values) are consistent, the verification is successful.

基於此原則，可以構建驗證機制：首先，礦工生成文本；然後，驗證者隨機選擇文本中的單詞，並要求礦工在此時提供“精神狀態”（即，相應的logitts進度標記標記標記哈希）。然後，驗證者在同一模型和上下文中對單詞進行推理，以生成自己的“精神狀態”。如果兩者的“精神狀態”（由哈希值表示）是一致的，則驗證是成功的。

This proof-of-work mechanism is consistent with the design principles of Bitcoin: mining (in this case, repeatedly executing the model through inference of 4,000 tokens) is expensive, but the verification process is very cheap (only 1 token is required for inference). This mechanism not only improves efficiency, but also ensures the security and reliability of verification.

這種工作證明機制與比特幣的設計原理一致：採礦（在這種情況下，通過推斷4,000個令牌反复執行模型）很昂貴，但是驗證過程非常便宜（推理只需要1個令牌）。這種機制不僅提高了效率，而且還確保了驗證的安全性和可靠性。