REPOGEO REPORT · LITE
test-time-training/ttt-lm-pytorch
Default branch main · commit cd831db1 · scanned 6/25/2026, 10:43:26 AM
GitHub: 1,378 stars · 83 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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.
- hightopics#1Add specific topics to improve categorization
Why:
COPY-PASTE FIXpytorch, rnn, sequence-modeling, test-time-training, ttt-layers, machine-learning, deep-learning, nlp, language-models
- highreadme#2Clarify the primary purpose of the PyTorch implementation in the README
Why:
CURRENTThis 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. For training code, or to replicate results from our paper, please view our JAX codebase. For inference kernels, or to replicate speed benchmarks from our paper, please view our kernel implementations.
COPY-PASTE FIXThis repository contains the official PyTorch model implementation of **Test-Time Training (TTT) layers**, a novel approach to sequence modeling with linear complexity and expressive hidden states. These TTT layers, detailed in 'Learning to (Learn at Test Time): RNNs with Expressive Hidden States', are designed for researchers and practitioners exploring advanced RNN architectures in PyTorch. While this codebase is suitable for experimentation and unoptimized inference, we recommend our JAX codebase for training and paper replication, and our dedicated kernel implementations for optimized inference benchmarks.
- mediumreadme#3Add a concise tagline under the README H1
Why:
CURRENT# Learning to (Learn at Test Time): RNNs with Expressive Hidden States
COPY-PASTE FIX# Learning to (Learn at Test Time): RNNs with Expressive Hidden States _Official PyTorch implementation of novel Test-Time Training (TTT) layers for efficient, expressive sequence modeling._
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.
- Performer · recommended 2×
- Reformer · recommended 2×
- nn.TransformerEncoder · recommended 2×
- nn.LSTM · recommended 2×
- nn.GRU · recommended 2×
- CATEGORY QUERYWhat are efficient sequence modeling layers with linear complexity for long contexts?you: not recommendedAI recommended (in order):
- Mamba
- RetNet
- Performer
- Linformer
- Reformer
- LSTM
- GRU
- Hyena Hierarchy
AI recommended 8 alternatives but never named test-time-training/ttt-lm-pytorch. This is the gap to close.
Show full AI answer
- CATEGORY QUERYHow to implement RNNs in PyTorch with improved expressive power for long sequence modeling?you: not recommendedAI recommended (in order):
- nn.Transformer
- nn.TransformerEncoder
- nn.TransformerDecoder
- nn.LSTM
- nn.GRU
- Hugging Face transformers library
- Reformer
- Longformer
- Performer
- nn.RNN
- nn.TransformerEncoder
- nn.LSTM
- nn.GRU
- nn.Conv1d
- nn.TransformerXL
AI recommended 15 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 completenesswarn
Suggestion:
- README presencepass
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?passAI 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?passAI 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?passAI 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?
Embed your GEO score
Drop this badge into the README of test-time-training/ttt-lm-pytorch. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/test-time-training/ttt-lm-pytorch)<a href="https://repogeo.com/en/r/test-time-training/ttt-lm-pytorch"><img src="https://repogeo.com/badge/test-time-training/ttt-lm-pytorch.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
test-time-training/ttt-lm-pytorch — 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