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The Bayesian Linguistic Forecaster (BLF) introduces a novel agentic architecture that integrates structured natural-language evidence with probability estimates to enhance binary forecasting accuracy. By leveraging a search-based ReAct framework and rigorous backtesting against historical data, this approach demonstrates superior performance over crowd-sourced baselines while addressing critical issues like data leakage. This work advances the field of AI agents by formalizing how linguistic context can be systematically utilized to refine predictive models in complex environments.
Agentic Forecasting with Structured Linguistic Beliefs
arXiv:2604.18576v5 Announce Type: replace Abstract: We propose the Bayesian Linguistic Forecaster (BLF), which is a search-based ReAct style agent for (binary) forecasting that augments its context with a linguistic 'belief state', consisting of a probability estimate and a \emph{structured} natural-language evidence summary. (The belief update is 'Bayesian-inspired' rather than exact Bayesian inference, although we do include a comparison to a more explicit Bayesian approach using LLM-elicited likelihood surrogates.) On 400 historical ForecastBench question-date instances, BLF has the highest overall Brier Index among the compared external agents, and is the only compared method to significantly outperform a crowd+empirical-prior baseline in terms of overall performance. Our backtesting framework combines date-aware search tools, leakage auditing, and paired comparisons for variance control to provide a rigorous empirical framework for comparing agentic forecasting systems.
Abstract:We propose the Bayesian Linguistic Forecaster (BLF), which is a search-based ReAct style agent for (binary) forecasting that augments its context with a linguistic 'belief state', consisting of a probability estimate and a \emph{structured} natural-language evidence summary. (The belief update is 'Bayesian-inspired' rather than exact Bayesian inference, although we do include a comparison to a more explicit Bayesian approach using LLM-elicited likelihood surrogates.) On 400 historical ForecastBench question-date instances, BLF has the highest overall Brier Index among the compared external agents, and is the only compared method to significantly outperform a crowd+empirical-prior baseline in terms of overall performance. Our backtesting framework combines date-aware search tools, leakage auditing, and paired comparisons for variance control to provide a rigorous empirical framework for comparing agentic forecasting systems.
v5 is a complete rewrite! Shorter and clearer, and includes new experiments measuring effect of the belief state, and comparison to a "proper" Bayesian approach (based on LLM likelihoods)
Artificial Intelligence (cs.AI)
arXiv:2604.18576 [cs.AI]
(or arXiv:2604.18576v5 [cs.AI] for this version)
v3 was published in ICML AI Forecasting workshop 2026 (https://forecasting-workshop.github.io/)
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