RRepoGEO

REPOGEO REPORT · LITE

test-time-training/ttt-lm-pytorch

Default branch main · commit cd831db1 · scanned 5/14/2026, 9:02:45 PM

GitHub: 1,368 stars · 84 forks

AI VISIBILITY SCORE
28 /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
2 / 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 test-time-training/ttt-lm-pytorch, 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 specific topics for better categorization

    Why:

    COPY-PASTE FIX
    ["pytorch", "test-time-training", "rnn", "sequence-modeling", "language-models", "expressive-hidden-states", "linear-complexity", "inference"]
  • highreadme#2
    Reposition README opening to highlight unique approach and purpose

    Why:

    CURRENT
    This is the official PyTorch model implementation of Learning to (Learn at Test Time): RNNs with Expressive Hidden States. We **do not recommend training** with this codebase, because it is written in pure PyTorch without any systems optimization, so training will be slow, especially when the per-device batch size is small.
    COPY-PASTE FIX
    This repository provides the official PyTorch implementation of **Test-Time Training (TTT) layers** for RNNs with expressive hidden states, offering a novel approach to **linear-complexity sequence modeling** that adapts during inference. It is designed for researchers and practitioners interested in exploring the TTT concept and its application to language models, particularly for **inference** where models adapt to new data distributions.
  • mediumabout#3
    Add homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://[YOUR_PAPER_URL_HERE]

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 test-time-training/ttt-lm-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Performer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Performer · recommended 2×
  2. Linformer · recommended 2×
  3. Reformer · recommended 1×
  4. Longformer · recommended 1×
  5. BigBird · recommended 1×
  • CATEGORY QUERY
    What are efficient alternatives to self-attention for sequence modeling with long contexts?
    you: not recommended
    AI recommended (in order):
    1. Performer
    2. Linformer
    3. Reformer
    4. Longformer
    5. BigBird
    6. FlashAttention

    AI recommended 6 alternatives but never named test-time-training/ttt-lm-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What PyTorch libraries offer linear-time recurrent models for long-range dependencies?
    you: not recommended
    AI recommended (in order):
    1. S4 (Structured State Space Sequences)
    2. H3 (Hungry Hungry Hippos)
    3. Retentive Networks (RetNet)
    4. RWKV (Receptance Weighted Key Value)
    5. Linformer
    6. Performer
    7. Nyströmformer

    AI recommended 7 alternatives but never named test-time-training/ttt-lm-pytorch. 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 test-time-training/ttt-lm-pytorch?
    pass
    AI named test-time-training/ttt-lm-pytorch explicitly

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

  • If a team adopts test-time-training/ttt-lm-pytorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named test-time-training/ttt-lm-pytorch 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 test-time-training/ttt-lm-pytorch solve, and who is the primary audience?
    pass
    AI did not name test-time-training/ttt-lm-pytorch — 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?

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