Guidance is All You Need: Temperature-Guided Reasoning in Large Language Models

TL;DR

This paper introduces a temperature-guided reasoning architecture for large language models, using hot and cold tokens to improve logical reasoning and efficiency, backed by theoretical guarantees and empirical results.

Contribution

It proposes the Token Temperature Mechanism and Guided Sequence of Thought, a novel approach that dynamically modulates token importance for enhanced reasoning.

Findings

Significant improvements in reasoning accuracy

Enhanced computational efficiency

Theoretical convergence guarantees

Abstract

We present Quasar-1, a novel architecture that introduces temperature-guided reasoning to large language models through the Token Temperature Mechanism (TTM) and Guided Sequence of Thought (GSoT). Our approach leverages the concept of hot and cold tokens, where hot tokens are prioritized for their contextual relevance, while cold tokens provide supplementary information. This dynamic modulation of token importance enables the model to achieve superior logical reasoning capabilities compared to traditional chain-of-thought approaches. Through rigorous mathematical analysis, we prove that our temperature-guided attention mechanism converges to optimal reasoning paths with exponential guarantees. Empirical results show significant improvements in reasoning accuracy and computational efficiency across a wide range of tasks, making advanced AI reasoning accessible to a broader range of applications.