Backed by top-tier institutions including Pantera Capital, YZi Lab, and OKX Ventures, aPriori is redefining the core beliefs underlying decentralized trading. The project’s founding team brings together talent from Jump, Coinbase, Citadel Securities, and dYdX—melding native blockchain technology with real-world Wall Street high-frequency trading expertise. On high-performance public blockchain networks, aPriori is building next-generation trade execution infrastructure to empower DeFi with truly competitive trading technology.

aPriori is revolutionizing the on-chain trading workflow. Leveraging an AI-powered DEX aggregator and an MEV-enabled liquidity staking module, aPriori integrates the entire order lifecycle—from placement to matching to yield—within a seamlessly sustainable product ecosystem.

After launching the AI-driven DEX aggregator Swapr last week, the aPriori team has now set its sights on the “intelligent core” of on-chain trading: the Order Flow Segmentation system. By combining behavioral tagging, wallet clustering, AI analytics, and on-chain feedback, this system ensures each transaction is processed with greater intelligence and fairness—shielding users from toxic flows (malicious activities like arbitrage slippage), and directing liquidity where it’s most needed. This not only makes trading smarter, but also instills greater order and trust throughout the on-chain market.

“Understanding every transaction is the foundation of fair execution.”

Order Flow Segmentation is one of aPriori’s core innovations. By analyzing trading behavior, wallet histories, and market responses, it can proactively determine if a transaction reflects genuine user activity or forms of toxic flow, such as arbitrage or sandwich attacks, even before execution. Unlike traditional methods that only check for transaction completion, this proactive filtering mitigates risk earlier, offers LPs safer counterparties, and enhances both path selection and execution fairness.

“Technology plus ecosystem: Monad’s best moment.”

Every public blockchain ecosystem features unique data characteristics. Solana is known for its fast transactions and vibrant user base, but the prevalence of closed-source contracts limits accessible data for model training. In contrast, Ethereum and other EVM chains offer open data but face throughput constraints, leading to more conservative trading patterns and lower data density.

Monad strikes a rare balance between speed and transparency: it combines Solana-like high throughput and aggressive trading strategies with the readability and openness of EVM infrastructure. This creates fertile ground for aPriori to develop the next generation of order flow intelligence models.

“User data isn’t just participation—it’s helping train the next wave of trading intelligence.”

Community Data Contribution Program: To further AI’s ability to understand trading behaviors, aPriori launched a data contribution initiative inviting the community to participate. By completing a few easy actions, any user can help the models gain deeper insights into the on-chain world:

• Bind a wallet: Link your main wallet addresses to provide a complete behavioral profile.
• Supported networks: Ethereum, BNB Chain, Monad Testnet.
• Sync social accounts: Optionally connect Twitter, Discord, and more for added identity context.
• Check-ins & task tracking: A dedicated dashboard displays your check-in history, trading activities, and contribution progress.

This data enables the system to identify which addresses belong to the same user, spot collaborative activity, and improve the AI’s ability to classify transaction types and assess risk.

“How can you tell if a transaction involves toxic flow?”

Within Swapr’s core engine, every transaction is evaluated by AI risk models before execution, primarily measuring:

• Trade specifics: buy/sell direction, token routing, gas usage, fees, slippage.
• Address history: trading frequency, prior behavior, asset movements.
• Market reactions: price movement from 1 second to 24 hours after execution.
• Profitability: whether the trade yields profit in different periods, and if it could harm LPs.

The system flags toxic flow—such as arbitrage or sandwich attacks exploiting information asymmetry—by evaluating each transaction’s threat to system fairness.

“The smartest models understand trading—they don’t just add complexity.”

aPriori employs a multi-algorithm approach, integrating traditional models (XGBoost, LightGBM) and temporal models (RNN, Transformer). Traditional models excel at interpretable, structured data analysis, while temporal models identify behavioral changes over time.

Swapr ultimately implements an ensemble architecture, in which sub-models learn from varied data and time windows. Integrated scoring enables more accurate and robust responses to sophisticated trading behaviors.

“Who’s really behind a transaction—colluding for arbitrage?”

Arbitrage rarely occurs through a single wallet; it’s usually a coordinated effort involving multiple addresses. By identifying these behavioral cohorts, the system can anticipate potential arbitrage syndicates and prevent concentrated toxic flows from impacting LPs.

“Making AI a core part of trade execution”

With richer training data, Swapr’s order flow intelligence is becoming a hallmark feature in DeFi routing, providing not just better pricing but also real-time liquidity management and balanced protection for both users and LPs.

Founder Ray notes, “A true DeFi execution engine can interpret and assess trades—and knows how to protect the system itself. We want Swapr to be the first trading entry point that can truly ‘think.’”

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