RRepoGEO

REPOGEO REPORT · LITE

BytedTsinghua-SIA/CUDA-Agent

Default branch main · commit 473025c8 · scanned 6/3/2026, 11:47:58 AM

GitHub: 967 stars · 79 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 BytedTsinghua-SIA/CUDA-Agent, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    cuda, agentic-ai, reinforcement-learning, gpu-programming, kernel-generation, deep-learning, llm-agents, high-performance-computing, code-generation
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    (Create a LICENSE file in the repository root with a standard open-source license like MIT or Apache-2.0 to clarify usage terms.)
  • highreadme#3
    Reposition the README's opening to highlight agentic RL differentiation

    Why:

    CURRENT
    CUDA-Agent is the first known RL-trained model to surpass advanced models such as Claude Opus-4.6 and Gemini 3 Pro on high-performance CUDA kernel generation.
    COPY-PASTE FIX
    CUDA-Agent is the first known **agentic reinforcement learning (RL) model** to surpass advanced LLMs like Claude Opus-4.6 and Gemini 3 Pro on high-performance CUDA kernel generation. Unlike traditional GPU compilers or frameworks (e.g., TVM, Triton, XLA), CUDA-Agent leverages an RL-trained agent to autonomously generate, debug, and optimize CUDA code, achieving state-of-the-art results on KernelBench and consistently outperforming the torch.compile baseline.

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 BytedTsinghua-SIA/CUDA-Agent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TorchInductor
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TorchInductor · recommended 2×
  2. Apache TVM · recommended 1×
  3. Triton · recommended 1×
  4. Tensor Comprehensions · recommended 1×
  5. XLA · recommended 1×
  • CATEGORY QUERY
    How can I automatically generate optimized GPU kernels for deep learning workloads?
    you: not recommended
    AI recommended (in order):
    1. Apache TVM
    2. Triton
    3. Tensor Comprehensions
    4. TorchInductor
    5. XLA
    6. MLIR
    7. CUDA C++
    8. cuBLAS
    9. cuDNN
    10. CUTLASS

    AI recommended 10 alternatives but never named BytedTsinghua-SIA/CUDA-Agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best AI-driven tools for generating highly efficient GPU code?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Triton
    2. TVM
    3. TorchInductor
    4. TensorFlow XLA
    5. NVIDIA Nsight Compute
    6. NVIDIA Nsight Systems
    7. OpenAI JAX

    AI recommended 7 alternatives but never named BytedTsinghua-SIA/CUDA-Agent. 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 BytedTsinghua-SIA/CUDA-Agent?
    pass
    AI did not name BytedTsinghua-SIA/CUDA-Agent — 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 BytedTsinghua-SIA/CUDA-Agent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named BytedTsinghua-SIA/CUDA-Agent 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 BytedTsinghua-SIA/CUDA-Agent solve, and who is the primary audience?
    pass
    AI named BytedTsinghua-SIA/CUDA-Agent explicitly

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

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BytedTsinghua-SIA/CUDA-Agent — 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