RRepoGEO

REPOGEO REPORT · LITE

goombalab/hnet

Default branch main · commit 3673fe12 · scanned 6/5/2026, 1:48:25 AM

GitHub: 853 stars · 101 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 goombalab/hnet, 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
  • highreadme#1
    Reposition README opening to clarify H-Net's purpose

    Why:

    CURRENT
    # H-Net

<table width="100%">
  <tr>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td></td>
    <td></td>
  </tr>
</table>

> **Dynamic Chunking for End-to-End Hierarchical Sequence Modeling**
> Sukjun Hwang, Brandon Wang, Albert Gu
> Paper: https://arxiv.org/abs/2507.07955
    COPY-PASTE FIX
    # H-Net: Hierarchical Network with Dynamic Chunking

H-Net is a novel PyTorch architecture designed for efficient hierarchical sequence modeling using dynamic chunking. It aims to improve long-range dependency and scalability in large language models and other sequence tasks.

> **Dynamic Chunking for End-to-End Hierarchical Sequence Modeling**
> Sukjun Hwang, Brandon Wang, Albert Gu
> Paper: https://arxiv.org/abs/2507.07955
  • hightopics#2
    Add specific topics to the repository

    Why:

    COPY-PASTE FIX
    pytorch, deep-learning, sequence-modeling, hierarchical-networks, dynamic-chunking, llm, large-language-models
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2507.07955

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 goombalab/hnet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Reformer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Reformer · recommended 2×
  2. Performer · recommended 2×
  3. BigBird · recommended 2×
  4. H3 (Hierarchical Hexagonal Grid System) · recommended 1×
  5. Longformer · recommended 1×
  • CATEGORY QUERY
    How to model long sequences efficiently using hierarchical structures and dynamic chunking?
    you: not recommended
    AI recommended (in order):
    1. H3 (Hierarchical Hexagonal Grid System)
    2. Longformer
    3. Reformer
    4. Performer
    5. BigBird
    6. Hierarchical Attention Networks (HANs)
    7. Hi-Transformer
    8. Differentiable Neural Computers (DNCs)
    9. Neural Turing Machines (NTMs)

    AI recommended 9 alternatives but never named goombalab/hnet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are modern PyTorch architectures for improved long-range dependency in sequence models?
    you: not recommended
    AI recommended (in order):
    1. Transformer
    2. LSTM
    3. GRU
    4. Reformer
    5. Performer
    6. Linformer
    7. BigBird

    AI recommended 7 alternatives but never named goombalab/hnet. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 goombalab/hnet?
    pass
    AI named goombalab/hnet explicitly

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

  • If a team adopts goombalab/hnet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named goombalab/hnet 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 goombalab/hnet solve, and who is the primary audience?
    pass
    AI named goombalab/hnet explicitly

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

Embed your GEO score

Drop this badge into the README of goombalab/hnet. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/goombalab/hnet.svg)](https://repogeo.com/en/r/goombalab/hnet)
HTML
<a href="https://repogeo.com/en/r/goombalab/hnet"><img src="https://repogeo.com/badge/goombalab/hnet.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

goombalab/hnet — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite