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LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory research paper by Google DeepMind, 2026

Google DeepMind · Mar 3, 2026 · Reasoning · 35 citations · 63 upvotes · unverified

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What it shows

LoGeR enables long-term 3D video reconstruction by combining bidirectional priors with a hybrid memory system that includes parametric Test-Time Training and non-parametric sliding window attention mechanisms.

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

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About this paper
Authors
Junyi Zhang, Charles Herrmann, Junhwa Hur and 5 more
arXiv
2603.03269 · PDF
Venue
arXiv.org
Citations
35, 4 influential · Semantic Scholar
Upvotes
63 · Hugging Face
Code
github.com/Junyi42/LoGeR
Lab
Google DeepMind · on Companies · on Acquisitions

Changes

What changed
Influential citationsfirst count: 4Sep 25, 2026
Citationsfirst count: 35Sep 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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