RRepoGEO

REPOGEO REPORT · LITE

radarFudan/Awesome-state-space-models

Default branch main · commit d4dd5c2b · scanned 5/30/2026, 5:28:06 PM

GitHub: 621 stars · 21 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface radarFudan/Awesome-state-space-models, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    state-space-models, ssm, large-language-models, llm, neural-networks, deep-learning, awesome-list, research-papers, hybrid-models, rnn, transformer-alternatives
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license, such as MIT or Apache-2.0.
  • highreadme#3
    Clarify the README's opening sentence to specify its purpose and audience

    Why:

    CURRENT
    # Awesome-state-space-models

Collection of papers/repos on state-space models, hybrid models.
    COPY-PASTE FIX
    # Awesome-state-space-models

A curated collection of research papers and repositories on state-space models and hybrid architectures for researchers and practitioners.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface radarFudan/Awesome-state-space-models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mamba
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Mamba · recommended 2×
  2. Hyena Hierarchy · recommended 2×
  3. S4 (Structured State Space Sequence Models) · recommended 1×
  4. H3 (Hungry Hungry Hippos) · recommended 1×
  5. RetNet (Retentive Network) · recommended 1×
  • CATEGORY QUERY
    How can state-space models enhance the efficiency and performance of large language models?
    you: not recommended
    AI recommended (in order):
    1. Mamba
    2. S4 (Structured State Space Sequence Models)
    3. H3 (Hungry Hungry Hippos)
    4. RetNet (Retentive Network)
    5. RWKV (Receptance Weighted Key Value)
    6. Hyena Hierarchy

    AI recommended 6 alternatives but never named radarFudan/Awesome-state-space-models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking recent research on alternative neural network architectures beyond standard transformers for sequential data.
    you: not recommended
    AI recommended (in order):
    1. Mamba
    2. Retentive Networks
    3. RWKV
    4. Hyena Hierarchy
    5. LongNet
    6. Recurrent Memory Transformers
    7. Striped Attention

    AI recommended 7 alternatives but never named radarFudan/Awesome-state-space-models. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of radarFudan/Awesome-state-space-models?
    pass
    AI did not name radarFudan/Awesome-state-space-models — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts radarFudan/Awesome-state-space-models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named radarFudan/Awesome-state-space-models explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo radarFudan/Awesome-state-space-models solve, and who is the primary audience?
    pass
    AI did not name radarFudan/Awesome-state-space-models — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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radarFudan/Awesome-state-space-models — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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