arXiv Artificial Intelligence

HHR: Hierarchical Hash Retrieval for Efficient LLM Generation

1 hour ago 02.10.2026 07:00 👁 1 views
HHR: Hierarchical Hash Retrieval for Efficient LLM Generation

Quick summary

arXiv:2610.01230v1 Announce Type: new Abstract: Efficient long-context inference is essential for large language models (LLMs), yet it poses a severe computational bottleneck. Hash-based retrieval offers an efficient alternative by encoding queries and keys into binary codes and using Hamming distance for key selection. However, this leads to a critical mismatch between Hamming distance and attention relevance. Query-Key logits depend jointly on directional similarity and feature magnitudes, whereas hash binarization discards magnitude information, causing both false-positive retrieval of low-

Key takeaways

  • arXiv:2610.01230v1 Announce Type: new Abstract: Efficient long-context inference is essential for large language models (LLMs), yet it poses a severe computational bottleneck.
  • Hash-based retrieval offers an efficient alternative by encoding queries and keys into binary codes and using Hamming distance for key selection.
  • However, this leads to a critical mismatch between Hamming distance and attention relevance.

Why it matters

This development shows AI moving deeper into everyday software. Productivity potential should be weighed against price, data permissions, exportability and the preservation of human control.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗