# yyyujintang/Awesome-Mamba-Papers

Awesome Papers related to  Mamba.

Repository: https://github.com/yyyujintang/Awesome-Mamba-Papers
Canonical: https://ross.abutalabs.com/products/awesome-mamba-papers
License Family: other
Last push: 2024-10-17T14:58:55+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 67
- inputs: {"age_days": 948, "days_push": 685, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1399, forks 73 (observed 2026-08-28T04:04:36.972528+00:00)

## What it is
A curated awesome-list of research papers on Mamba and state space models (SSMs), organized by topic with links to papers, code, and conference acceptances. It is actively updated with notes on papers from venues like ICML, CVPR, and MICCAI.

## Use cases
- find papers on mamba state space models
- research transformer alternatives for long sequences
- keep up with new SSM architecture papers
- find vision mamba papers from CVPR or ECCV
- survey state space models for medical imaging
- get started learning about selective state space models

## When to choose
- you need a curated, categorized reading list of Mamba/SSM research
- you want paper links plus accompanying code repositories
- you want conference-annotated entries (ICML, CVPR, MICCAI)

## When to avoid
- you need runnable Mamba implementation code rather than a paper list
- you need tutorials or documentation rather than research papers
- you need exhaustive coverage guaranteed in real time

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: deep-learning, machine-learning, artificial-intelligence, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, mamba, state-space-models, papers, research, ssm

## Member repositories
- yyyujintang/Awesome-Mamba-Papers (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:36.972528+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:39:11.783593+00:00, confidence not recorded.
  - readme: https://github.com/yyyujintang/Awesome-Mamba-Papers (fetched 2026-08-28T04:04:36.972528+00:00, sha 3d6e9e2174a1)
- Data as of 2026-08-30T08:39:29.467469+00:00.