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0. Introduction
The term “intent” has recently become a hot topic among researchers and on crypto twitter. In this article, we will introduce the concept of intent and how it relates to Hyper Oracle’s zkAutomation and zkML.
1. Intents
a) Definition
The idea of intents is actually less clear than rollups, but at least it's not controversial. Understanding intents can be quite confusing, but the explanations provided here aim to help you grasp it completely.
By comparisons, ordinary transactions are imperative (describing the process). In a swap transaction, without using intents, the user signs a transaction that sends the specified amount to a router address. Then, the amount is converted to WETH, and finally, the WETH is exchanged for a specific number of other tokens. However, with intents, the whole description becomes simpler because intents are declarative (describing the outcome) in nature. For example, a user can simply state their intent as follows: "pay 1 ETH in exchange for 2 tokens."
If you find the concept of intents confusing, it's okay to continue reading with a sense of confusion, or read more about the definition.
b) Similar Concepts and Example
Intents, as a concept, share a philosophical foundation with various older concepts that have been in existence since the rise of search engines.
While these concepts share a common idea, intent goes beyond limit orders and serves as a broader term that encapsulates the fundamental meaning of these concepts.
Practically, an intent is an off-chain signed message that encodes which state transitions a user wants to achieve. Basically a user signs a message "any state transition that satisfies [these conditions] has my approval".
c) Advantages
The intents-based design pattern offers several advantages:
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Improved user experience (UX) and execution quality: With intents, users only need to approve their desired outcome, without getting involved in complex operations. The resolver takes care of implementing the intent using the most optimal path, such as utilizing AMM routing, using USDC for gas, or executing cross-chain smart contracts.
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Enhanced efficiency: The flexibility of the off-chain resolver component contributes to improved efficiency. Within the same off-chain environment, multiple intents can potentially offset each other or generate revenue economies, leading to overall efficiency gains. Additionally, off-chain computations are not constrained by on-chain computation limitations, enabling the implementation of complex calculations off-chain before settling them on-chain.
2. Resolver of Intents
a) Everything is a Resolver
Let's revisit the definition of intents. Intents are conditions of satisfaction or transactions with free variables, often fulfilled by off-chain actors.
When we examine the role of the off-chain resolver in the intents process, we can observe that the intent-based architecture shares similarities with a rollup architecture. Both architectures employ off-chain computation to enhance the user experience and achieve a more favorable UX!
In line with this, we can observe that SUAVE, ERC-4337 AA, and SeaPort are all intent mode applications. They all need to have at least one party who is incentivized to execute and resolve the intent in a timely manner within a specified schema. The party is (often) off-chain, and referred to by different names such as a resolver, solver, sequencer, or other names.
b) Automation Network as Resolver
Another important intents-based design and application type is: an automation network is a resolver. For example, any Gelato Network-based application is designed using intents.
In this scenario, intents are derived from the off-chain definition of an automation schema. Intents encompass the automation job, while the resolver, as an off-chain node within the automation network, adhere to the specified schema. The resolver's role is to assemble the provided data into a transaction that is settled on-chain.
3. zkAutomation & zkML, Trustless Resolver of Intents
However, it is important to note that the resolver in automation networks often introduces an additional trust factor. There is no way to directly trust that the resolver will handle intents accurately and efficiently without errors. The complete security of the resolver cannot be guaranteed. To mitigate this concern, other automation networks typically employ governance mechanisms or legal agreements to ensure that the resolver operates in a trustworthy manner.
Taking the example of Flashbots, a specialized system for Miner Extractable Value (MEV) on Ethereum, they have explored four distinct solutions to address the security issue associated with off-chain resolvers.
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Cryptoeconomics (Staking & Slashing)
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Multisig
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Trusted Hardware
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Pure Crypto (ZK)
In summary, while ZK (Zero-Knowledge) solutions are considered the most secure and trustless option, they may not be deemed production-ready in the context of Flashbots.
However, in our automation network scenario, ZK is the ideal solution we are seeking to ensure that off-chain resolver computations are entirely verifiable and trustless. We have developed a Minimum Viable Product (MVP) and a demonstration for this product, which can be accessed through the provided links (MVP and demo).
At Hyper Oracle, we go beyond resolving automation intents and extend our capabilities to trustlessly resolve machine learning computation intents as well.
a) zkAutomation, Trustless Resolver of Automation Intents
zkAutomation is Hyper Oracle’s trustless automation protocol based on zero knowledge proofs. zkAutomation's automation execution is entirely secured by zk, and the automation sources (data sources that trigger the automation) are fully customizable using zkGraph.
b) zkML, Trustless Resolver of ML Intents
As mentioned earlier, the resolver has the capability to facilitate arbitrary heavy computations. This includes the ability to perform machine learning computations in a trustless off-chain environment.
By executing these computations off-chain, the process can be conducted securely and with transparency. Once the computations are completed, the results can then be settled on-chain, ensuring the integrity and reliability of the machine learning inference process.
c) zkAutomation and zkML with Intents
From an intent perspective, zkAutomation and zkML are trustless resolvers of intents.
In short, zkAutomation and zkML are off-chain executions, but fully secured by zk and decentralized.
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