@inproceedings{zhang-etal-2026-compevo,
title = "{C}omp{E}vo: Competition-Induced Evolution for Multi-Agent in News-Driven Time Series Forecasting",
author = "Zhang, Yuxuan and
Feng, Yangyang and
Guan, Yong and
Li, Daifeng and
Zhang, Kexin and
Chen, Junlan and
Deng, Bowen and
Liu, Jun and
Zeng, Zehua",
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.2354/",
pages = "50581--50607",
ISBN = "979-8-89176-462-0",
abstract = "News-driven time series forecasting (NTSF) uses evolving textual events together with historical observations to predict future values, supporting applications such as market risk monitoring and resource scheduling. In multi-agent settings, two challenges still remain. The first is degeneration of thought, where agents converge to similar evidence-seeking behaviors. The second is insufficient theoretical grounding, where strategy updates are often heuristic and lack a principled formulation. To address the above challenges, we propose CompEvo, a competition-induced evolution framework for multi-agent NTSF. At the theoretical level, our evolutionary game formulation establishes equilibrium existence and local convergence under the stated assumptions. At the methodological level, we construct a trainable multi-agent evolution framework that integrates strategy execution, fitness-based differentiable selection, and competition-induced strategy evolution. CompEvo enables heterogeneous agents to explore diverse news evidence, converts forecasting feedback into differentiable influence weights, and evolves agent strategies under competitive pressure to preserve effective logic while maintaining diversity. Experiments on four real-world datasets show that CompEvo reduces RMSE by 29.8{\%} and MAPE by 30.3{\%} on average relative to the strongest multi-agent baseline. Further analysis indicates that CompEvo maintains diverse and specialized agent behaviors."
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<abstract>News-driven time series forecasting (NTSF) uses evolving textual events together with historical observations to predict future values, supporting applications such as market risk monitoring and resource scheduling. In multi-agent settings, two challenges still remain. The first is degeneration of thought, where agents converge to similar evidence-seeking behaviors. The second is insufficient theoretical grounding, where strategy updates are often heuristic and lack a principled formulation. To address the above challenges, we propose CompEvo, a competition-induced evolution framework for multi-agent NTSF. At the theoretical level, our evolutionary game formulation establishes equilibrium existence and local convergence under the stated assumptions. At the methodological level, we construct a trainable multi-agent evolution framework that integrates strategy execution, fitness-based differentiable selection, and competition-induced strategy evolution. CompEvo enables heterogeneous agents to explore diverse news evidence, converts forecasting feedback into differentiable influence weights, and evolves agent strategies under competitive pressure to preserve effective logic while maintaining diversity. Experiments on four real-world datasets show that CompEvo reduces RMSE by 29.8% and MAPE by 30.3% on average relative to the strongest multi-agent baseline. Further analysis indicates that CompEvo maintains diverse and specialized agent behaviors.</abstract>
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%0 Conference Proceedings
%T CompEvo: Competition-Induced Evolution for Multi-Agent in News-Driven Time Series Forecasting
%A Zhang, Yuxuan
%A Feng, Yangyang
%A Guan, Yong
%A Li, Daifeng
%A Zhang, Kexin
%A Chen, Junlan
%A Deng, Bowen
%A Liu, Jun
%A Zeng, Zehua
%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 zhang-etal-2026-compevo
%X News-driven time series forecasting (NTSF) uses evolving textual events together with historical observations to predict future values, supporting applications such as market risk monitoring and resource scheduling. In multi-agent settings, two challenges still remain. The first is degeneration of thought, where agents converge to similar evidence-seeking behaviors. The second is insufficient theoretical grounding, where strategy updates are often heuristic and lack a principled formulation. To address the above challenges, we propose CompEvo, a competition-induced evolution framework for multi-agent NTSF. At the theoretical level, our evolutionary game formulation establishes equilibrium existence and local convergence under the stated assumptions. At the methodological level, we construct a trainable multi-agent evolution framework that integrates strategy execution, fitness-based differentiable selection, and competition-induced strategy evolution. CompEvo enables heterogeneous agents to explore diverse news evidence, converts forecasting feedback into differentiable influence weights, and evolves agent strategies under competitive pressure to preserve effective logic while maintaining diversity. Experiments on four real-world datasets show that CompEvo reduces RMSE by 29.8% and MAPE by 30.3% on average relative to the strongest multi-agent baseline. Further analysis indicates that CompEvo maintains diverse and specialized agent behaviors.
%U https://aclanthology.org/2026.findings-emnlp.2354/
%P 50581-50607
Markdown (Informal)
[CompEvo: Competition-Induced Evolution for Multi-Agent in News-Driven Time Series Forecasting](https://aclanthology.org/2026.findings-emnlp.2354/) (Zhang et al., Findings 2026)
ACL
- Yuxuan Zhang, Yangyang Feng, Yong Guan, Daifeng Li, Kexin Zhang, Junlan Chen, Bowen Deng, Jun Liu, and Zehua Zeng. 2026. CompEvo: Competition-Induced Evolution for Multi-Agent in News-Driven Time Series Forecasting. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 50581–50607, Budapest, Hungary. Association for Computational Linguistics.