Temporal Horizon and how to steer it

This project was a done as a part of SPAR cohort. I have already performed all the required representational and geometrical analysis of temporal horizon across 7 different LLMs. The focus of this project is to understand how temporal horizon is encoded in LLMs and how can we better steer it. A synthetic dataset was generated which classified temporal horizon into short and long (call it H1) and a graded dataset (call it H2) which quantized the temporal horizon spectrum into points for better analysis. Activation Patching, Divergence tests, Probe training, modality tests and representational alignment tests agree and establish that temporal horizon is a property that is build upon through the model depth where the most focus is given to it in the middle layers of the model. A geometrical analysis of the model shows that the temporal horizon get stretched out progressively while also being unstretched and untwisted through the model depth. A quantitative analysis of manifold and linear steering is done which reveals linear steering has lower-to-similar naturaleness compared to manifold steering. A qualitative analysis has to be done to understand where is steering the most effective and help generalize temporal horizon manipulation.