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MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models research paper by NVIDIA, 2026

NVIDIA · May 14, 2026 · Multimodal and robotics · 2 citations · 77 upvotes · unverified

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What it shows

A new benchmark evaluates memory capabilities in vision-language models through multi-session conversations, revealing limitations of both long-context and memory-augmented approaches.

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

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About this paper
Authors
Xiyu Ren, Zhaowei Wang, Yiming Du and 11 more
arXiv
2605.14906 · PDF
Venue
arXiv.org
Citations
2, 0 influential · Semantic Scholar
Upvotes
77 · Hugging Face
Code
github.com/xrenaf/MEMLENS
Lab
NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 2Sep 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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