@inproceedings{chai-etal-2026-agentic,
title = "Agentic Time Machine as an Infrastructure for Future-Event Forecasting",
author = "Chai, Jingyi and
Zheng, Bingyang and
Liu, Xiangrui and
Lu, Hao and
Zhou, Zihang and
Wang, Tianchen and
Zhang, Kemeng and
Chen, Siheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2020/",
pages = "46016--46035",
ISBN = "979-8-89176-459-0",
abstract = "Forecasting future events is a critical challenge for large language model (LLM) agents, spanning domains from elections and monetary policy to financial markets. However, evaluating progress on this task presents a fundamental trade-off between efficiency and environment fidelity. While live evaluation benchmarks suffer from an inherently slow feedback loop, existing retrospective replays typically restrict agents to static, pre-frozen databases that sacrifice the environmental realism of actual deployments. To tackle this issue, we introduce Agentic Time Machine (TM), an infrastructure that approximates the pre-cutoff web state at any chosen past time by filtering post-cutoff content. Leveraging this evaluation infrastructure, we further propose a planner-solver-aggregator multi-agent framework that breaks each question into diverse analytical angles, gathers evidence in parallel, and combines the results into a single forecast. Experiments show that offline scores under TM correlate strongly with live FutureX scores, validating that TM offers a fast and reliable sandbox for forecasting-agent evaluation. On FutureX-Past and Polymarket evaluated under TM, our framework achieves the highest score among strong closed-book, tool-augmented, and self-consistency baselines. On the live FutureX leaderboard, our system achieves the best average rank over four consecutive weeks, ranking 1st in May Week 1."
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<abstract>Forecasting future events is a critical challenge for large language model (LLM) agents, spanning domains from elections and monetary policy to financial markets. However, evaluating progress on this task presents a fundamental trade-off between efficiency and environment fidelity. While live evaluation benchmarks suffer from an inherently slow feedback loop, existing retrospective replays typically restrict agents to static, pre-frozen databases that sacrifice the environmental realism of actual deployments. To tackle this issue, we introduce Agentic Time Machine (TM), an infrastructure that approximates the pre-cutoff web state at any chosen past time by filtering post-cutoff content. Leveraging this evaluation infrastructure, we further propose a planner-solver-aggregator multi-agent framework that breaks each question into diverse analytical angles, gathers evidence in parallel, and combines the results into a single forecast. Experiments show that offline scores under TM correlate strongly with live FutureX scores, validating that TM offers a fast and reliable sandbox for forecasting-agent evaluation. On FutureX-Past and Polymarket evaluated under TM, our framework achieves the highest score among strong closed-book, tool-augmented, and self-consistency baselines. On the live FutureX leaderboard, our system achieves the best average rank over four consecutive weeks, ranking 1st in May Week 1.</abstract>
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%0 Conference Proceedings
%T Agentic Time Machine as an Infrastructure for Future-Event Forecasting
%A Chai, Jingyi
%A Zheng, Bingyang
%A Liu, Xiangrui
%A Lu, Hao
%A Zhou, Zihang
%A Wang, Tianchen
%A Zhang, Kemeng
%A Chen, Siheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chai-etal-2026-agentic
%X Forecasting future events is a critical challenge for large language model (LLM) agents, spanning domains from elections and monetary policy to financial markets. However, evaluating progress on this task presents a fundamental trade-off between efficiency and environment fidelity. While live evaluation benchmarks suffer from an inherently slow feedback loop, existing retrospective replays typically restrict agents to static, pre-frozen databases that sacrifice the environmental realism of actual deployments. To tackle this issue, we introduce Agentic Time Machine (TM), an infrastructure that approximates the pre-cutoff web state at any chosen past time by filtering post-cutoff content. Leveraging this evaluation infrastructure, we further propose a planner-solver-aggregator multi-agent framework that breaks each question into diverse analytical angles, gathers evidence in parallel, and combines the results into a single forecast. Experiments show that offline scores under TM correlate strongly with live FutureX scores, validating that TM offers a fast and reliable sandbox for forecasting-agent evaluation. On FutureX-Past and Polymarket evaluated under TM, our framework achieves the highest score among strong closed-book, tool-augmented, and self-consistency baselines. On the live FutureX leaderboard, our system achieves the best average rank over four consecutive weeks, ranking 1st in May Week 1.
%U https://aclanthology.org/2026.emnlp-main.2020/
%P 46016-46035
Markdown (Informal)
[Agentic Time Machine as an Infrastructure for Future-Event Forecasting](https://aclanthology.org/2026.emnlp-main.2020/) (Chai et al., EMNLP 2026)
ACL
- Jingyi Chai, Bingyang Zheng, Xiangrui Liu, Hao Lu, Zihang Zhou, Tianchen Wang, Kemeng Zhang, and Siheng Chen. 2026. Agentic Time Machine as an Infrastructure for Future-Event Forecasting. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 46016–46035, Budapest, Hungary. Association for Computational Linguistics.