TimeSeek: Temporal Reliability of Agentic Forecasters
Summary: arXiv:2604.04220v1 Announce Type: new
Abstract: We introduce TimeSeek, a benchmark for studying how the reliability of agentic LLM forecasters changes over a prediction market’s lifecycle. We evaluate 10 frontier models on 150 CFTC-regulated Kalshi binary markets at five temporal checkpoints, with and without web search, for 15,000 forecasts total. Models are most competitive early in a market’s life and on high-uncertainty markets, but much less competitive near resolution and on strong-consensus markets. Web search improves pooled Brier Skill Score (BSS) for every model overall, yet hurts in 12% of model-checkpoint pairs, indicating that retrieval is helpful on average but not uniformly so. Simple two-model ensembles reduce error without surpassing the market overall. These descriptive results motivate time-aware evaluation and selective-deference policies rather than a single market snapshot or a uniform tool-use setting.
Introduction
TimeSeek represents a significant advancement in the field of prediction markets and agentic forecasting models. By establishing a systematic benchmark, it allows researchers and practitioners to better understand the dynamics of forecasting performance throughout the evolution of prediction markets. This article explores the findings of the TimeSeek benchmark and its implications for future research and application.
Methodology
The evaluation involved ten state-of-the-art models applied to 150 binary markets regulated by the Commodity Futures Trading Commission (CFTC). The models were tested at five distinct temporal checkpoints to ascertain how performance varied over time. The analysis encompassed a total of 15,000 forecasts, allowing for a robust comparison across different models and market conditions.
Key Findings
The results of the TimeSeek benchmark yielded several important insights:
- Early Market Advantage: Models demonstrated heightened competitiveness in the initial stages of market life, indicating that they are more adept at making accurate predictions when uncertainty is higher.
- Challenges Near Resolution: As markets approached resolution, model performance diminished significantly, particularly in scenarios with strong consensus among participants.
- Impact of Web Search: Incorporating web search functionality generally enhanced the pooled Brier Skill Score (BSS) for all models. However, it also showed detrimental effects in 12% of model-checkpoint pairs, highlighting variability in retrieval effectiveness.
- Ensemble Approach: Utilizing simple two-model ensembles was found to reduce forecasting error, although they did not outperform the market’s collective predictions.
Implications for Future Research
The findings from TimeSeek underline the necessity for time-aware evaluation strategies in the realm of predictive modeling. Researchers are encouraged to consider the lifecycle of prediction markets when assessing model performance. The variations in reliability suggest that a one-size-fits-all approach to model evaluation may not be effective.
Moreover, the data advocate for selective-deference policies, where forecasters can be chosen based on performance metrics relevant to specific market conditions and timeframes. This approach could help in optimizing the use of predictive tools in various contexts.
Conclusion
TimeSeek marks a pivotal step towards understanding the intricacies of agentic forecasting models within prediction markets. By providing a comprehensive framework for evaluation, it sets the stage for future advancements and refinements in predictive analytics. As the landscape of forecasting continues to evolve, the insights gained from TimeSeek will serve as a valuable resource for researchers and practitioners alike.