RRepoGEO

REPOGEO REPORT · LITE

raymin0223/mixture_of_recursions

Default branch main · commit 53d0fee4 · scanned 5/13/2026, 12:13:05 PM

GitHub: 571 stars · 83 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 raymin0223/mixture_of_recursions, 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
    Add an explicit introductory sentence to the README to clarify the repo's purpose.

    Why:

    COPY-PASTE FIX
    This repository provides the official PyTorch implementation of the Mixture-of-Recursions (MoR) model, as presented in our NeurIPS 2025 paper, focusing on adaptive token-level computation for efficient LLMs.
  • mediumreadme#2
    Introduce a 'Key Features' section to highlight the implementation's specific contributions.

    Why:

    COPY-PASTE FIX
    ## ✨ Key Features of this Repository
    - **Official PyTorch Implementation:** Reproduce the results from our NeurIPS 2025 paper.
    - **Mixture-of-Recursions Model:** Explore dynamic recursive depths for adaptive token-level computation.
    - **KV Cache Handling:** See our novel solution for the missing Key-Value cache problem in early-exiting.
    - **Router Mechanism:** Understand how tokens are dynamically routed through the model.
  • lowreadme#3
    Add a brief comparison section to the README.

    Why:

    COPY-PASTE FIX
    ## 🆚 Comparison to Existing Adaptive Computation Methods
    Mixture-of-Recursions distinguishes itself from traditional early-exiting methods by directly addressing the Key-Value (KV) cache problem, which often limits the practical applicability of dynamic token-level computation. Unlike methods that approximate or recompute KV pairs, MoR learns dynamic recursive depths to adaptively manage computation while maintaining KV cache integrity for subsequent tokens.

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 raymin0223/mixture_of_recursions
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/DeepSpeed
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/DeepSpeed · recommended 1×
  2. facebookresearch/fairseq · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. openai/triton · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How can I make large language models run faster and more cost-effectively with adaptive computation?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. Fairseq (facebookresearch/fairseq)
    3. Hugging Face Transformers (huggingface/transformers)
    4. OpenAI Triton (openai/triton)
    5. PyTorch FSDP (pytorch/pytorch)
    6. TensorRT
    7. ONNX Runtime (microsoft/onnxruntime)

    AI recommended 7 alternatives but never named raymin0223/mixture_of_recursions. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are approaches for implementing early exit mechanisms in LLMs to save computational resources?
    you: not recommended
    AI recommended (in order):
    1. Google's Switch Transformers
    2. Fairseq
    3. DeepSpeed
    4. BranchyNet
    5. Hugging Face Transformers
    6. PyTorch
    7. TensorFlow
    8. Adaptive Computation Time (ACT)
    9. DistilBERT
    10. TinyBERT
    11. PaddlePaddle
    12. PaddleSlim
    13. ONNX Runtime
    14. TensorFlow Serving
    15. TorchServe

    AI recommended 15 alternatives but never named raymin0223/mixture_of_recursions. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 raymin0223/mixture_of_recursions?
    pass
    AI did not name raymin0223/mixture_of_recursions — 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 raymin0223/mixture_of_recursions in production, what risks or prerequisites should they evaluate first?
    pass
    AI named raymin0223/mixture_of_recursions 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 raymin0223/mixture_of_recursions solve, and who is the primary audience?
    pass
    AI named raymin0223/mixture_of_recursions explicitly

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

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raymin0223/mixture_of_recursions — 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