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

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 demonstrates that properly guided reasoning paths, modulated by learned token temperatures, are sufficient 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.

Introduction. Recent advances in large language models have demonstrated remarkable capabilities in natural language processing tasks [1, 2]. However, existing approaches often lack structured reasoning mechanisms that can guarantee logical consistency and optimal solution paths. We introduce Quasar-1, a novel architecture that addresses these limitations through temperature-guided reasoning, providing theoretical guarantees for convergence and optimality.

Discussion / Conclusion. We have presented a rigorous mathematical framework for temperature-guided reasoning in language models. Our theoretical analysis demonstrates superior bounds compared to existing approaches, with empirical results validating our theoretical predictions. Future work will explore extensions to non-Euclidean temperature spaces and informationtheoretic bounds on token selection.