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April 12, 2026Open Access

Think Less, Store Smarter: A Theoretical Framework for Type-Aware KV Cache Quantization in Large Reasoning Models

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RNRaviteja Nekkalapu

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Overview

This framework shows that uniform KV cache quantization is suboptimal in reasoning models, implying better performance with tailored approaches.

Key Points

  • The aim is to establish a theoretical framework, the Think-Answer Quantization Gap, for optimizing KV cache quantization in large reasoning models.
  • Introduced the Think-Answer Quantization Gap (TAQG) framework.
  • Proved the suboptimality of uniform KV cache quantization under certain conditions.
  • Validated the framework using DeepSeek-R1-Distill-Qwen-1.5B model.
  • Found that answer-phase tokens showed higher cosine redundancy than think-phase tokens in the tested model.
  • Observed a model-size-dependent reversal in token redundancy compared to findings on the larger 671B model.

Cite This Study

Raviteja Nekkalapu (2026) studied this question.

synapsesocial.com/papers/69db36c24fe01fead37c4cbahttps://doi.org/10.5281/zenodo.19500603
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