@inproceedings{zhong-etal-2026-foredreamer,
title = "{F}ore{D}reamer: A Self-Evolving Dual-Agent Memory Architecture for Future Event Prediction",
author = "Zhong, Linhao and
Du, Zongze and
Wu, Linyu and
Bo, Yu and
Li, Hourong and
Jing, Chenchen and
Chen, Hao and
Xi, Yuling and
Shen, Chunhua",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.147/",
pages = "3085--3112",
ISBN = "979-8-89176-462-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T ForeDreamer: A Self-Evolving Dual-Agent Memory Architecture for Future Event Prediction
%A Zhong, Linhao
%A Du, Zongze
%A Wu, Linyu
%A Bo, Yu
%A Li, Hourong
%A Jing, Chenchen
%A Chen, Hao
%A Xi, Yuling
%A Shen, Chunhua
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhong-etal-2026-foredreamer
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.147/
%P 3085-3112
Markdown (Informal)
[ForeDreamer: A Self-Evolving Dual-Agent Memory Architecture for Future Event Prediction](https://aclanthology.org/2026.findings-emnlp.147/) (Zhong et al., Findings 2026)
ACL
- Linhao Zhong, Zongze Du, Linyu Wu, Yu Bo, Hourong Li, Chenchen Jing, Hao Chen, Yuling Xi, and Chunhua Shen. 2026. ForeDreamer: A Self-Evolving Dual-Agent Memory Architecture for Future Event Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 3085–3112, Budapest, Hungary. Association for Computational Linguistics.