Reshaping On-chain Dynamics: How Monad's aPriori Initiative Leads Transaction Innovation with AI, Launching Data Contribution Program in Sync

2025-08-06 09:30

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“Transaction speed is not the end goal; fairness and efficiency are the underlying beliefs of DeFi”

Source: aPriori

Backed heavily by top institutions such as Pantera Capital, YZi Lab, OKX Ventures, aPriori is reshaping the underlying beliefs of decentralized trading. The project's core team members come from Jump, Coinbase, Citadel Securities, and dYdX, combining on-chain native technology with Wall Street high-frequency trading experience. aPriori is building a next-generation trade execution system on a high-performance public chain, injecting truly competitive trading infrastructure into DeFi.

aPriori is completely rewriting the on-chain trading process: through an AI-driven DEX aggregator and an MEV-supported liquidity staking module, aPriori transforms the order flow from ordering, matching to profit into a closed loop, integrated into a sustainable product system.

Following the team's launch of the AI-driven DEX aggregator Swapr last week, aPriori has set its sights on the "brain" of on-chain transactions, which is the Order Flow Segmentation system. This system combines behavioral tagging, wallet clustering, AI analysis, and on-chain feedback mechanisms, aiming to intelligently and fairly process every transaction, avoiding harm from "toxic flow" such as arbitrage slippage, and directing liquidity to where it is most needed. It not only makes trading smarter but also brings order and trust to the entire on-chain market flow.

Understanding every transaction is the starting point of fair execution.

Order flow recognition is one of aPriori's core technologies, which predicts before a transaction occurs whether it is a normal user operation or "toxic flow" such as arbitrage or sandwich attacks through analyzing transaction behavior, wallet history, and market reactions. Compared to the traditional method of only looking at whether a transaction is executed, this identification method can filter potential risks earlier, provide LPs with safer counterparties, and enhance path selection and execution fairness.

Technology + Ecosystem: The Perfect Moment for Monad

The data characteristics of different public chain ecosystems vary: Solana has high-speed transactions and active users, but due to many closed-source contracts, it limits the data available for training; Ethereum and other EVM chains, while having open data, are limited by performance bottlenecks, with overall transaction behavior being conservative and data density being low.

The Monad achieves a rare balance between performance and transparency - combining Solana-style high throughput and aggressive transaction style while retaining the readability and openness brought by the EVM architecture. This provides an ideal foundation for aPriori to build the next-generation order flow recognition model.

「Users' data not only participate but also train the next generation of transaction intelligence.」

Community Data Contribution Program: To train AI to intelligently recognize transaction behavior, aPriori has launched a community-engaged data contribution program. Every user can help the model better understand the on-chain world by completing simple actions.

· Connect Wallet: Link users' commonly used wallet addresses to provide a more complete behavioral view;

· Supported Chains: Ethereum, BNB Chain, Monad Testnet;

· Sync Social Accounts: Optionally link Twitter, Discord, etc., to provide additional identity clues;

· Check-in and Task Tracking: Exclusive dashboard displaying user check-in records, transaction behavior, and contribution progress.

This data can help the system determine which addresses belong to the same user, whether there is cooperative operation, and enhance AI's ability to identify transaction types and risks.

「How to determine if a transaction contains a toxic flow?」

In Swapr's core engine, each transaction is evaluated for risk by an AI model before confirmation, mainly referring to the following points:

· The Transaction Itself: Buy/sell direction, coin path, gas, fee, slippage, etc.;

· Address History: Transaction frequency, past behaviors, asset changes;

· Market Reaction: Price trend 1 second to 24 hours after the transaction;

· Profit Assessment: Whether this transaction was profitable at different time intervals, whether it could harm LP.

The model will identify whether each transaction belongs to "toxic flow," such as arbitrage or sandwich attacks, informationally advantaged trading behaviors, and assess their potential threat to system fairness.

「The value of a model lies not in its complexity, but in its understanding of transactions.」

From a rule engine to AI neural networks: aPriori does not stick to a single algorithm, but integrates both traditional models (XGBoost, LightGBM) and time series models (RNN, Transformer). The former efficiently interprets structured data and is highly explainable, while the latter excels at capturing behavioral changes in time series.

Swapr ultimately adopts a model ensemble architecture, where different sub-models learn in their respective data dimensions and time windows. By merging the scores, it can more accurately respond to complex trading behaviors.

「Behind every transaction, who is conspiring for arbitrage?」

Arbitrage behavior is usually not carried out by a single wallet, but is the result of multiple addresses working together. By identifying these "behavioral groups," the system can anticipate potential arbitrage groups and prevent "toxic flow" from disproportionately impacting LP.

「Let AI be part of the trade execution」

With rich training data, Swapr's identification system is becoming a core differentiator in DeFi routing. It not only brings better pricing but also dynamically adjusts liquidity direction to protect the interests of both users and LP.

Founder Ray emphasizes: "A true DeFi execution engine understands, can make judgments, and knows how to protect the system. We hope Swapr will be the first trading interface that can 'think'."