Streaming Attention Approximation via Discrepancy Theory

📰 ArXiv cs.AI

Researchers propose BalanceKV, a streaming algorithm for approximating attention computations in large language models

advanced Published 25 Mar 2026
Action Steps
  1. Understand the challenges of high memory requirements in large language models
  2. Apply BalanceKV algorithm for epsilon-approximating attention computations
  3. Use geometric process for selecting a balanced set of keys and values
  4. Evaluate the streaming complexity of attention approximation
Who Needs to Know This

ML researchers and engineers working on large language models can benefit from this research to improve the efficiency of token generation, and software engineers can apply the findings to develop more scalable AI systems

Key Insight

💡 BalanceKV enables efficient attention approximation in large language models

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💡 BalanceKV: a streaming algorithm for approximating attention computations in LLMs

Key Takeaways

Researchers propose BalanceKV, a streaming algorithm for approximating attention computations in large language models

Full Article

Title: Streaming Attention Approximation via Discrepancy Theory

Abstract:
arXiv:2502.07861v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved impressive success, but their high memory requirements present challenges for long-context token generation. In this paper we study the streaming complexity of attention approximation, a key computational primitive underlying token generation. Our main contribution is BalanceKV, a streaming algorithm for $\epsilon$-approximating attention computations based on geometric process for selecting a ba
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