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Li-Qin Ye

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#artificial intelligence Preprint Sep 2026

Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents

We introduce Forecast-Dojo, a replayable environment for benchmarking and training LLM forecasting agents. It combines resolved prediction-market questions with dated news, allowing agents to research an event and revisit their predictions at successive historical dates. The same tasks and tools support repeated evalua...

Li-Qin Ye, Hao-Rui Wang, Fardin Ahmed et al. · 0 citations
#artificial intelligence Preprint Aug 2026

Can LLMs Take the Pulse of the Economy? A Real-Time Evaluation of LLM Nowcasts on Macroeconomic Indicators

Over six months with four state-of-the-art LLM agents configured with web search, aggregate nowcast accuracy is broadly comparable to the institutional and professional benchmarks, with performance varying widely across individual indicators.

Xin-Yue Zhao, Rui-Yi Zhang, Li-Qin Ye et al. · 1 citation

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