RRepoGEO

REPOGEO REPORT · LITE

HKUDS/LightReasoner

Default branch main · commit fbd55c7d · scanned 6/6/2026, 5:33:38 PM

GitHub: 610 stars · 33 forks

AI VISIBILITY SCORE
40 /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
3 / 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 HKUDS/LightReasoner, 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
    Reposition the README H1 to be a direct statement of purpose

    Why:

    CURRENT
    <h1 align="center"> <br> 💡 LightReasoner: Can <strong><em>SMALL</em></strong> Language Models Teach <strong><em>LARGE</em></strong> Language Models Reasoning? </h1>
    COPY-PASTE FIX
    <h1 align="center"> 💡 LightReasoner: Enhancing Large Language Model Reasoning with Small, Efficient Models </h1>
  • mediumtopics#2
    Add more specific topics to improve category matching

    Why:

    CURRENT
    large-language-models, post-training, reasoning-models, token-efficiency
    COPY-PASTE FIX
    large-language-models, post-training, reasoning-models, token-efficiency, slm-to-llm, model-distillation, knowledge-transfer, efficient-llm-reasoning
  • lowabout#3
    Rephrase the 'about' description for clarity and directness

    Why:

    CURRENT
    [ACL 2026 Oral] "LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?"
    COPY-PASTE FIX
    LightReasoner explores how small language models can teach large language models reasoning, achieving superior performance with remarkable token efficiency. (ACL 2026 Oral)

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 HKUDS/LightReasoner
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. bitsandbytes · recommended 1×
  3. pytorch/pytorch · recommended 1×
  4. OpenVINO · recommended 1×
  5. huggingface/peft · recommended 1×
  • CATEGORY QUERY
    How to enhance large language model reasoning capabilities with smaller, efficient models?
    you: not recommended
    AI recommended (in order):
    1. transformers (huggingface/transformers)
    2. bitsandbytes
    3. PyTorch (pytorch/pytorch)
    4. OpenVINO
    5. PEFT (huggingface/peft)
    6. DeepSpeed
    7. Neo4j
    8. TypeDB

    AI recommended 8 alternatives but never named HKUDS/LightReasoner. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to improve large language model reasoning post-training for better token efficiency.
    you: not recommended
    AI recommended (in order):
    1. FLAN
    2. T0
    3. Hugging Face Transformers
    4. QLoRA
    5. GPTQ
    6. AWQ
    7. FAISS
    8. Pinecone
    9. Weaviate
    10. GSM8K
    11. MATH
    12. ARC
    13. PPO
    14. DPO

    AI recommended 14 alternatives but never named HKUDS/LightReasoner. 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 HKUDS/LightReasoner?
    pass
    AI named HKUDS/LightReasoner explicitly

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

  • If a team adopts HKUDS/LightReasoner in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HKUDS/LightReasoner 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 HKUDS/LightReasoner solve, and who is the primary audience?
    pass
    AI named HKUDS/LightReasoner 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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HKUDS/LightReasoner — 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