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

TL;DR

Guidance is All You Need introduces Quasar-1, a temperature-guided reasoning framework for large language models that combines a Token Temperature Mechanism ($\mathcal{T}$) with a Guided Sequence of Thought (GSoT) to dynamically highlight crucial tokens and prune reasoning paths. The authors provide rigorous mathematical foundations, including discrete temperature dynamics, invariance, contraction-based convergence, and stability analyses, along with a temperature-guided attention mechanism that converges to fixed points at exponential rates. Empirically, Quasar-1 demonstrates improved reasoning accuracy and computational efficiency across tasks, with detailed architectural, training, and implementation considerations for practical deployment. The work shows that adaptive temperature modulation can yield faster, more scalable reasoning with robust theoretical guarantees, potentially broadening access to advanced AI reasoning in resource-constrained settings.

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.

Figures (6)

• Figure 1: Temperature values change across model layers, highlighting important tokens as reasoning progresses.
• Figure 2: Visualization of token temperatures in a sentence, emphasizing subject-object pairs in a semantic parsing task.
• Figure 3: Decision tree of reasoning paths, highlighting the selected optimal path with token temperatures at each step.
• Figure 4: Side-by-side comparison of CoT vs. TTM+GSoT for a specific reasoning task.
• Figure 5: Quasar-1 Architecture Overview: Temperature-guided attention mechanism integrated with transformer layers

Theorems & Definitions (30)

• Definition 1: Context-Dependent Temperature
• Theorem 2: Discrete Temperature Evolution
• proof
• Theorem 3: Temperature Invariance
• proof
• Theorem 4: Strong Convergence
• proof
• Definition 5: Token Temperature Function
• Theorem 6: Temperature-Guided Attention
• Theorem 7: Temperature Convergence