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TriAttention: Efficient Long Reasoning with Trigonometric KV Compression research paper by NVIDIA, 2026

NVIDIA · Apr 6, 2026 · Reasoning · 18 citations · 112 upvotes · unverified

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What it shows

TriAttention addresses KV cache memory bottlenecks in LLMs by leveraging Q/K vector concentration in pre-RoPE space to improve key importance estimation and enable efficient long-context generation.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Weian Mao, Xi Lin, Wei Huang and 5 more
arXiv
2604.04921 · PDF
Venue
arXiv.org
Citations
18, 4 influential · Semantic Scholar
Upvotes
112 · Hugging Face
Code
github.com/WeianMao/triattention
Lab
NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 4Sep 25, 2026
Citationsfirst count: 18Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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