Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols

2026-04-22

AI Summary

This study addresses the lack of fine-grained modeling of on-chain event-driven price formation mechanisms in automated market maker (AMM) protocols. The authors present the first annotated dataset encompassing multiple AMM protocols and over 8.9 million on-chain events. They propose a novel loss function that integrates temporal uncertainty by combining an inter-block interval regression term with uncertainty-weighted mean squared error. Built upon homoscedastic weighting and a temporal point process (TPP) architecture, the method reduces time prediction error by an average of 56.41% across eight state-of-the-art TPP models while preserving event-type prediction accuracy. This work establishes a new benchmark for event-driven modeling in decentralized finance (DeFi).

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Mechanism Design
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Automated Market Makers (AMMs), as a core infrastructure of decentralized finance (DeFi), uniquely drive on-chain asset pricing through a deterministic reserve ratio mechanism. Unlike traditional markets, AMM price dynamics is triggered largely by on-chain events (e.g., swap) that change the reserve ratio, rather than by continuous responses to off-chain information. This makes event-level analysis crucial for understanding price formation mechanisms in AMMs. However, existing research generally neglects the micro-structural dynamics at the AMMs level, lacking both a comprehensive dataset covering multiple protocols with fine-grained event classification and an effective framework for event-aware modeling. To fill this gap, we construct a dataset containing 8.9 million on-chain event records from four representative AMMs protocols: Pendle, Uniswap v3, Aave and Morpho, with precise annotations of transaction type and block height timestamps. Furthermore, we propose an Uncertainty Weighted Mean Squared Error (UWM) loss function, which incorporates the block interval regression term into the traditional Time-Point Process (TPP) objective function by weighting the uncertainty with homoscedasticity. Extensive experiments on eight advanced TPP architectures demonstrate that this loss function reduces the time prediction error by an average of 56.41\% while maintaining the accuracy of event type prediction, establishing a robust benchmark for event-aware prediction in the AMMs ecosystem. This work provides the necessary data foundation and methodological framework for modeling the discreteness and event-driven characteristics of on-chain price discovery. All datasets and source code are publicly available. https://github.com/yosen-king/Deep-AMM-Events

Problem

• Automated Market Makers
• event-aware forecasting
• on-chain events
• price formation
• DeFi

Innovation

• Automated Market Makers
• event-aware forecasting
• Uncertainty Weighted MSE
• on-chain events
• Temporal Point Process