TUCH: Temporal Unfolding via Causal Hypotheses for LLM-Based Forecasting

Forecasting requires estimating the probability of a future outcome from evidence that becomes available over time. Existing LLM-based forecasting methods remain largely temporally flat, organizing evidence without explicitly reasoning across successive stages of real-world time. We introduce temporal unfolding and instantiate it in TUCH, where causal hypotheses represent the model’s current estimate of the event’s causal state, are updated with new evidence, and guide subsequent retrieval. To provide step-level supervision, we construct reference trajectories from resolved forecasting questions and train the model with supervised fine-tuning and process-outcome reinforcement learning. Experiments on PROPHET and ForecastBench show that TUCH consistently outperforms same-backbone temporally flat baselines, while strong LLMs under the same temporal-unfolding scaffold show mixed performance relative to the trained TUCH models.