Heavily invested by top institutions such as Pantera Capital, YZi Lab, and OKX Ventures, aPriori is reconstructing the underlying belief of decentralized trading. With core team members from Jump, Coinbase, Citadel Securities, and dYdX, aPriori is building a new generation of trading execution system on high-performance public chains by integrating on-chain native technology with Wall Street high-frequency trading experience, 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 integrates the entire order cycle from placement to matching to revenue into a sustainable product ecosystem.
After launching the AI-driven DEX aggregator Swapr last week, aPriori has now focused on the "brain" of on-chain trading, namely the Order Flow Segmentation system. This system combines behavior tags, wallet clustering, AI analysis, and on-chain feedback mechanisms, aiming to make every transaction smarter and fairer, avoiding harm from "toxic flow" such as arbitrage slippage, while directing liquidity to where it should go. It not only makes trading smarter but also makes the entire on-chain market more orderly and trustworthy.
"Understanding every transaction is the starting point for fair execution."
Order flow identification is one of aPriori's core technologies. By analyzing transaction behaviors, wallet history, and market responses, it determines before a transaction occurs whether it belongs to normal user operations or "toxic flow" such as arbitrage or sandwich attacks. Compared to traditional methods that only look at whether a transaction is completed, this identification method can filter potential risks earlier, providing safer counterparties for LPs and improving path selection and execution fairness.
"Technology + Ecosystem: The Perfect Moment for Monad"
Different public chain ecosystems have distinct data characteristics: Solana has high-speed transactions and active users, but many contracts are closed-source, limiting available training data; Ethereum and other EVM chains have open data but are limited by performance bottlenecks, with overall conservative trading behaviors and low 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 an ideal soil for aPriori to build the next-generation order flow identification model.
"User data is not just participation, but training the next-generation trading intelligence."
Community Data Contribution Program: To train AI to more intelligently identify trading behaviors, aPriori has initiated 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: Optional association with Twitter, Discord, etc., to supplement more identity clues;
· Check-in and Task Tracking: Dedicated panel displaying user check-in records, trading behaviors, and contribution progress.
These data can help the system determine which addresses belong to the same user, whether there are coordinated operations, and improve AI's ability to identify transaction types and risks.
"How to determine if a transaction contains toxic flow?"
In Swapr's core engine, each transaction is assessed for risk by the AI model before confirmation, mainly referencing the following points:
· Transaction Itself: Buy/sell direction, token path, Gas, fees, slippage, etc.;
· Address History: Transaction frequency, past behaviors, asset changes;
· Market Response: Price trend from 1 second to 24 hours after the transaction;
· Profit Judgment: Whether this transaction is profitable at different time periods and potentially harmful to LPs.
The model identifies whether each transaction belongs to "toxic flow", such as arbitrage or sandwich attacks based on information advantages, judging its potential threat to system fairness.
"The model is not better with more complexity, but valuable when it truly understands trading."
From rule engines to AI neural networks: aPriori is not confined to a single algorithm but integrates traditional models (XGBoost, LightGBM) with temporal models (RNN, Transformer). The former efficiently handles structured data with good interpretability, while the latter is good at capturing behavioral changes in time series.
Swapr ultimately adopts an Ensemble architecture, where different sub-models learn in their respective data dimensions and time windows, and the merged score can more accurately handle 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 multiple addresses collaborating. By identifying these "behavior groups", the system can predict potential arbitrage groups and prevent "toxic flow" from concentrated attacks on LPs.
"Let AI become part of transaction execution"
As training data becomes richer, Swapr's identification system is becoming a core differentiator in DeFi routing. It not only provides better quotes but can also dynamically adjust 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'."
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