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Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization research paper by UC Berkeley, 2026

UC Berkeley · Feb 3, 2026 · Inference and efficiency · 16 citations · 35 upvotes · unverified

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What it shows

Quant VideoGen addresses KV cache memory limitations in autoregressive video diffusion models through semantic-aware smoothing and progressive residual quantization, achieving significant memory reduction with minimal latency impact.

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

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About this paper
Authors
Haocheng Xi, Shuo Yang, Yilong Zhao and 13 more
arXiv
2602.02958 · PDF
Venue
arXiv.org
Citations
16, 1 influential · Semantic Scholar
Upvotes
35 · Hugging Face
Code
github.com/svg-project/Quant-VideoGen
Lab
UC Berkeley

Changes

What changed
Influential citationsfirst count: 1Sep 25, 2026
Citationsfirst count: 16Sep 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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