Mamba: Linear-Time Sequence Modeling with Selective State Spaces research paper by Carnegie Mellon University, 2023

Carnegie Mellon University · Dec 1, 2023 · Architectures · 9,182 citations · 152 upvotes

A selective state space model that scales linearly with sequence length and matches Transformers on language.

Summarised by hand from the abstract.

Date	Paper	Lab	Citations
Jun 12, 2017	Attention Is All You NeedIntroduced the Transformer, the attention-only architecture behind nearly every large language model since.Google · Jun 2017Google	Google	194k
Aug 31, 2026	On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training StabilityQwen3.8-Flash-Next: a 125B mixture-of-experts model with 6B active that nearly matches its 397B predecessor at 1/9 the training compute.Alibaba (Qwen) · Aug 2026Alibaba (Qwen)	Alibaba (Qwen)	6

About this paper

Authors: Albert Gu, Tri Dao
arXiv: 2312.00752 · PDF
Venue: arXiv.org
Citations: 9,182, 1,312 influential · Semantic Scholar
Upvotes: 152 · Hugging Face
Code: github.com/state-spaces/mamba
Lab: Carnegie Mellon University

Changes

Date	What changed
Sep 24, 2026	New paperAdded to the listSep 24, 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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