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        <title>What Ensemble Brier Scores Hide: Evidence Routing at a Fixed Article-Exposure Budget in LLM Prediction-Market Forecasting</title>
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    <abstract>We ask whether giving different evidence to different LLM forecasters creates useful diversity when the total article-exposure count is held fixed. Across 708 resolved prediction-market contracts, we compare six-member ensembles whose forecasters either share the same two article pair or are distributed evenly across three pairs, with both conditions using twelve article exposures. On 609 pre-market-end contracts, evidence routing reliably increases within-ensemble dispersion for GPT-4.1-mini, Qwen3-30B, and OLMo-3.1-32B. An exact Brier-score decomposition separates changes in member error, dispersion, and ensemble error, while a grouped-variance analysis attributes nearly all of the dispersion increase to differences between assigned article pairs rather than repeated sampling. Greater dispersion, however, does not automatically improve the en- semble: only Qwen’s reduction in ensemble Brier survives multiplicity correction, while GPT and OLMo show no statistically robust net improvement. Post-hoc controls show that more balanced routing further increases dispersion at a fixed evidence union, while an alterna- tive Qwen readout changes channel attribution without establishing a different net effect. Evidence routing therefore consistently increases forecast diversity in this setting, but its value depends on whether that diversity outweighs any loss in individual forecast quality.</abstract>
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%0 Conference Proceedings
%T What Ensemble Brier Scores Hide: Evidence Routing at a Fixed Article-Exposure Budget in LLM Prediction-Market Forecasting
%A Aluguvelly, Avinash Goutham
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F aluguvelly-2026-ensemble
%X We ask whether giving different evidence to different LLM forecasters creates useful diversity when the total article-exposure count is held fixed. Across 708 resolved prediction-market contracts, we compare six-member ensembles whose forecasters either share the same two article pair or are distributed evenly across three pairs, with both conditions using twelve article exposures. On 609 pre-market-end contracts, evidence routing reliably increases within-ensemble dispersion for GPT-4.1-mini, Qwen3-30B, and OLMo-3.1-32B. An exact Brier-score decomposition separates changes in member error, dispersion, and ensemble error, while a grouped-variance analysis attributes nearly all of the dispersion increase to differences between assigned article pairs rather than repeated sampling. Greater dispersion, however, does not automatically improve the en- semble: only Qwen’s reduction in ensemble Brier survives multiplicity correction, while GPT and OLMo show no statistically robust net improvement. Post-hoc controls show that more balanced routing further increases dispersion at a fixed evidence union, while an alterna- tive Qwen readout changes channel attribution without establishing a different net effect. Evidence routing therefore consistently increases forecast diversity in this setting, but its value depends on whether that diversity outweighs any loss in individual forecast quality.
%U https://aclanthology.org/2026.finnlp-1.30/
%P 373-382

Markdown (Informal)

[What Ensemble Brier Scores Hide: Evidence Routing at a Fixed Article-Exposure Budget in LLM Prediction-Market Forecasting](https://aclanthology.org/2026.finnlp-1.30/) (Aluguvelly, FinNLP 2026)

• What Ensemble Brier Scores Hide: Evidence Routing at a Fixed Article-Exposure Budget in LLM Prediction-Market Forecasting (Aluguvelly, FinNLP 2026)

ACL

• Avinash Goutham Aluguvelly. 2026. What Ensemble Brier Scores Hide: Evidence Routing at a Fixed Article-Exposure Budget in LLM Prediction-Market Forecasting. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 373–382, Budapest, Hungary. Association for Computational Linguistics.