ForeDreamer: A Self-Evolving Dual-Agent Memory Architecture for Future Event Prediction

2026-08-21

AI Summary

本文提出ForeDreamer，一种自我进化双代理记忆架构，通过将原始网络证据转化为结构化记忆来改进未来事件预测，解决现有方法在处理噪声、冗余和不完整信息时的不足。

Abstract

Open-web future event prediction requires agents to distill reliable signals from noisy, redundant, and incomplete evidence. Existing retrieval/memory mechanisms directly feed retrieved information to agents or rely on simple memory functions such as storing and reusing prior information for prediction, leaving them insufficient for open-web forecasting. We propose to transform raw web evidence into structured memory before prediction, enabling agents to reason over distilled, question-specific evidence rather than noisy retrieval results. This paper presents ForeDreamer, a self-evolving dual-agent framework for managing memory over open-web evidence. ForeDreamer separates factual memory, a question-specific evidence state for the current forecast, from experiential memory, persistent agent experience accumulated across forecasting episodes. It uses a main agent for search and prediction, and a memory-processing subagent to convert search results into factual memory with dedicated tools. ForeDreamer further evolves experiential memory through two tracks, improving both forecasting decisions and factual-memory construction. Experiments on Prophet Arena and FutureX demonstrate the effectiveness of ForeDreamer. Project page: https://zhongzero.github.io/ForeDreamer

Problem

• future event prediction
• open-web forecasting
• memory mechanisms
• noisy evidence
• redundant information

Innovation

• Self-Evolving Dual-Agent
• Structured Memory
• Factual and Experiential Memory
• Open-Web Evidence