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
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.
- highreadme#1Add an explicit introductory sentence to the README to clarify the repo's purpose.
Why:
COPY-PASTE FIXThis 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#2Introduce 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#3Add 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.
- microsoft/DeepSpeed · recommended 1×
- facebookresearch/fairseq · recommended 1×
- huggingface/transformers · recommended 1×
- openai/triton · recommended 1×
- pytorch/pytorch · recommended 1×
- CATEGORY QUERYHow can I make large language models run faster and more cost-effectively with adaptive computation?you: not recommendedAI recommended (in order):
- DeepSpeed (microsoft/DeepSpeed)
- Fairseq (facebookresearch/fairseq)
- Hugging Face Transformers (huggingface/transformers)
- OpenAI Triton (openai/triton)
- PyTorch FSDP (pytorch/pytorch)
- TensorRT
- 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 QUERYWhat are approaches for implementing early exit mechanisms in LLMs to save computational resources?you: not recommendedAI recommended (in order):
- Google's Switch Transformers
- Fairseq
- DeepSpeed
- BranchyNet
- Hugging Face Transformers
- PyTorch
- TensorFlow
- Adaptive Computation Time (ACT)
- DistilBERT
- TinyBERT
- PaddlePaddle
- PaddleSlim
- ONNX Runtime
- TensorFlow Serving
- 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 completenesspass
- 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 raymin0223/mixture_of_recursions?passAI 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?passAI 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?passAI 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?
Embed your GEO score
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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