RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/LPLB

Default branch main · commit 0490f794 · scanned 6/7/2026, 8:42:37 PM

GitHub: 506 stars · 35 forks

AI VISIBILITY SCORE
35 /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
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 deepseek-ai/LPLB, 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 a clarifying sentence to the README's opening paragraph

    Why:

    CURRENT
    LPLB is a parallel load balancer that leverages linear programming to optimize expert parallel workload distribution for MoE (Mixture-of-Experts) models.
    COPY-PASTE FIX
    LPLB is a parallel load balancer that leverages linear programming to optimize expert parallel workload distribution for MoE (Mixture-of-Experts) models. **It is a system component for dynamic load balancing, not a dataset or evaluation script for LLMs.**
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    moe, mixture-of-experts, load-balancing, linear-programming, expert-parallelism, deep-learning, gpu-acceleration, hpc
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    [Insert URL of project homepage or related research paper here]

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 deepseek-ai/LPLB
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google's Switch Transformer
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google's Switch Transformer · recommended 1×
  2. DeepMind's GLaM · recommended 1×
  3. Fairseq · recommended 1×
  4. Google's GShard · recommended 1×
  5. Mixtral 8x7B · recommended 1×
  • CATEGORY QUERY
    How to dynamically balance workload distribution across experts in Mixture-of-Experts models?
    you: not recommended
    AI recommended (in order):
    1. Google's Switch Transformer
    2. DeepMind's GLaM
    3. Fairseq
    4. Google's GShard
    5. Mixtral 8x7B

    AI recommended 5 alternatives but never named deepseek-ai/LPLB. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient methods for optimizing expert parallel workload distribution using linear programming?
    you: not recommended
    AI recommended (in order):
    1. PuLP
    2. Google OR-Tools
    3. Gurobi Optimizer
    4. CPLEX Optimizer
    5. SciPy.optimize
    6. GLPK

    AI recommended 6 alternatives but never named deepseek-ai/LPLB. 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 deepseek-ai/LPLB?
    pass
    AI named deepseek-ai/LPLB explicitly

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

  • If a team adopts deepseek-ai/LPLB in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepseek-ai/LPLB 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 deepseek-ai/LPLB solve, and who is the primary audience?
    pass
    AI named deepseek-ai/LPLB explicitly

    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 deepseek-ai/LPLB. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/deepseek-ai/LPLB.svg)](https://repogeo.com/en/r/deepseek-ai/LPLB)
HTML
<a href="https://repogeo.com/en/r/deepseek-ai/LPLB"><img src="https://repogeo.com/badge/deepseek-ai/LPLB.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

deepseek-ai/LPLB — 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