You can improve LLM forecasting accuracy by identifying and amplifying time-awareness features inside the model, reducing the bias toward using information that shouldn't be available yet.
This paper uses sparse autoencoders to identify internal features in LLMs that drive forecasting behavior, distinguishing between time-aware reasoning and look-ahead bias. By steering these features, researchers show they can reduce the model's tendency to use future information while maintaining general reasoning ability.