Reasoning Palette: Modulating Reasoning via Latent Contextualization for Controllable Exploration for (V)LMs

The article introduces a method called "Reasoning Palette" for controlling and exploring the reasoning capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs). The core idea is to modulate reasoning by using latent contextualization. This suggests a focus on improving the controllability and interpretability of these models' reasoning processes. The use of "latent contextualization" implies a sophisticated approach to influencing the internal representations and decision-making of the models.

• •Focuses on improving the controllability and interpretability of LLM/VLM reasoning.
• •Employs "latent contextualization" for reasoning modulation.
• •The research is likely aimed at enhancing the exploration capabilities of LLMs and VLMs.