Original source: aPriori

Backed by top institutions such as Pantera Capital, YZi Lab, and OKX Ventures, aPriori is reconstructing the underlying belief of decentralized trading. The core team members come from Jump, Coinbase, Citadel Securities, and dYdX, combining on-chain native technology with practical experience in Wall Street high-frequency trading. aPriori is building a new generation of trading execution systems on high-performance public chains, 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 liquid staking module, aPriori integrates the order process from placing orders, matching, to profit closure into a sustainable product system.

After the team launched the AI-driven DEX aggregator Swapr last week, aPriori has now turned its attention to the "recognition brain" of on-chain trading, which is the Order Flow Segmentation system. This system combines behavioral tagging, wallet clustering, AI analysis, and on-chain feedback mechanisms, aiming to ensure that every transaction is processed more intelligently and fairly, avoiding harm from "toxic flow" such as arbitrage slippage, while directing liquidity to where it is most needed. It not only makes trading smarter but also brings more order and trust to the entire on-chain market.

"Understanding every transaction is the starting point for fair execution."

Order flow recognition is one of aPriori's core technologies. By analyzing trading behavior, wallet history, and market reactions, it assesses before a transaction occurs whether it belongs to normal user operations or is part of arbitrage, squeezing, or other "toxic flow." Compared to traditional methods that only look at whether a transaction is completed, this recognition method can filter potential risks earlier, providing LPs with safer counterparties and enhancing path selection and execution fairness.

"Technology + Ecosystem: The Perfect Timing for Monad"

The data characteristics of different public chain ecosystems vary: Solana has high-speed transactions and active users, but the large number of closed-source contracts limits the data available for training; Ethereum and other EVM chains have open data but are constrained by performance bottlenecks, resulting in conservative overall trading behavior and lower data density.

Monad achieves a rare balance between performance and transparency - combining Solana-like high throughput and aggressive trading style while retaining the readability and openness brought by the EVM architecture. This provides aPriori with the ideal soil to build the next generation of order flow recognition models.

"User data is not just participation; it is training the next generation of trading intelligence."

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

· Bind Wallet: Connect the user's 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 supplement more identity clues;

· Check-in and Task Tracking: A dedicated panel displays the user's check-in records, trading behavior, and contribution progress.

This data helps the system determine which addresses belong to the same user, whether there are collaborative operations, and enhances AI's ability to recognize transaction types and risks.

"How to determine if a transaction contains toxic flow?"

In the core engine of Swapr, each transaction undergoes risk assessment by the AI model before confirmation, primarily referencing the following points:

· The Transaction Itself: Buy/sell direction, token path, Gas, fees, slippage, etc.;

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

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

· Profit Assessment: Whether this transaction is profitable over different time periods and whether it could harm LPs.

The model identifies whether each transaction belongs to "toxic flow," such as arbitrage or squeezing based on information advantages, assessing its potential threat to system fairness.

"The model is not better the more complex it is, but the more it understands trading, the more valuable it becomes."

From rule engines to AI neural networks: aPriori does not stick to a single algorithm but integrates traditional models (XGBoost, LightGBM) with time series models (RNN, Transformer). The former efficiently handles structured data with interpretability, 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, and after merging scores, can respond more accurately to complex trading behaviors.

"Behind a transaction, who is colluding for arbitrage?"

Arbitrage behavior is usually not completed by a single wallet but is the result of coordinated operations by multiple addresses. By identifying these "behavior groups," the system can predict potential arbitrage groups and prevent concentrated impacts of "toxic flow" on LPs.

"Let AI be part of trading execution."

As training data becomes richer, Swapr's recognition system is becoming a core differentiator in DeFi routing. It not only brings better quotes but also dynamically adjusts liquidity direction, protecting the interests of both users and LPs.

Founder Ray emphasizes: "A true DeFi execution engine understands, can judge, and knows how to protect the system. We hope Swapr is the first trading entry that can 'think.'"

This article is from a submission and does not represent the views of BlockBeats.

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