Backed by top institutions such as Pantera Capital, YZi Lab, and OKX Ventures, aPriori is reconstructing the underlying belief in decentralized trading. Core team members come from Jump, Coinbase, Citadel Securities, and dYdX, combining on-chain native technology with real-world experience in Wall Street high-frequency trading. aPriori is building a next-generation trading execution system on high-performance public chains, injecting genuinely competitive trading infrastructure into DeFi.
aPriori is rewriting the on-chain trading process: through AI-driven DEX aggregators and MEV-supported liquidity staking modules, 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 turned its attention to the 'identification brain' of on-chain trading, which is the order flow identification system. This system combines behavioral labeling, wallet clustering, AI analysis, and on-chain feedback mechanisms, aiming to ensure that every transaction is processed more intelligently and fairly, avoiding harm from arbitrage slippage and other 'toxic flows,' while 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.
'Understanding each transaction is the starting point for fair execution.'
Order flow identification is one of aPriori's core technologies. By analyzing trading behavior, wallet history, and market reactions, it assesses whether a transaction is a normal user operation or a 'toxic flow' such as arbitrage or squeeze before it occurs. Compared to traditional methods that only look at whether a trade is executed, this recognition method can filter potential risks earlier, providing LPs with safer counterparties and improving path selection and execution fairness.
'Technology + Ecosystem: A perfect opportunity for Monad.'
The data characteristics of different public chain ecosystems vary: Solana has high-speed transactions and active users, but large amounts of closed-source contracts limit the data available for training; Ethereum and other EVM chains, while data is open, are constrained by performance bottlenecks, leading to overall conservative 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 ideal soil for aPriori to build the next generation of order flow identification models.
'User data is not just participation, but training the next generation of trading intelligence.'
Community data contribution plan: To train AI to identify trading behaviors more intelligently, aPriori has initiated a community participation data contribution plan. Every user can help the model better 'understand' the on-chain world by completing the following simple actions.
Bind wallets: connect 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 supplement more identity clues;
Check-in and task tracking: exclusive panel displaying user check-in records, trading behavior, and contribution progress.
This data can help the system determine which addresses belong to the same user, whether there are coordinated operations, and enhance AI's ability to identify transaction types and risks.
'How to determine if a transaction contains toxic flow?'
In the core engine of Swapr, each transaction is assessed for risk by the AI model before confirmation, primarily considering the following points:
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 in different time periods and whether it could harm LPs.
The model will identify whether each transaction belongs to 'toxic flow,' such as arbitrage or squeeze trades based on information advantages, assessing its potential threat to system fairness.
'A model is not better when it’s more complex, but rather more valuable when it understands trading.'
From rule engines to AI neural networks: aPriori is not limited 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 from 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 actions are usually not completed by a single wallet but are the result of multiple addresses cooperating. By identifying these 'behavior groups,' the system can predict potential arbitrage groups and prevent 'toxic flow' from concentrating its impact on LPs.
'Let AI be 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 directions, 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 will be the first trading entry that can 'think.'