Prior work has largely treated forecasting as a static task, failing to consider how forecasts and the confidence in them should evolve as new evidence emerges. To address this gap, we introduce EvolveCast, a framework for evaluating whether large language models revise their forecasts appropriately in response to new information. In particular, EvolveCast assesses whether LLMs update their forecasts when presented with information released after their training cutoff. We use human forecasters as a comparative reference to assess forecast updates and confidence calibration under new information. While LLMs demonstrate some responsiveness to new information, their updates are often inconsistent or overly conservative. We further find that both verbalized and logits-based confidence estimates remain far from the human reference standard. Across settings with a variety of LLMs, models tend to be conservative in updating their forecasts. These findings suggest that current approaches (e.g., RAG-based methods) for updating model knowledge are insufficient for probabilistic reasoning; models treat new information as retrieval context rather than evidence that shifts posterior probability. EvolveCast thus underscores the need for more robust mechanisms to incorporate external knowledge into belief dynamics.