RRepoGEO

REPOGEO REPORT · LITE

SeekingDream/Static-to-Dynamic-LLMEval

Default branch main · commit dcbd946a · scanned 6/17/2026, 4:47:38 PM

GitHub: 497 stars · 38 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 SeekingDream/Static-to-Dynamic-LLMEval, 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
    Clarify repo's purpose as a survey paper in README's opening

    Why:

    CURRENT
    # Recent Advances in Large Language Model Benchmarks against Data Contamination: From Static to Dynamic Evaluation
    COPY-PASTE FIX
    # Recent Advances in Large Language Model Benchmarks against Data Contamination: From Static to Dynamic Evaluation

This repository serves as the official, actively maintained companion for our survey paper, providing a comprehensive review and ongoing updates on the latest research in dynamic evaluation for large language models.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0) in the root of the repository to clearly state the terms of use for the code and content.
  • mediumtopics#3
    Add 'survey' and 'literature-review' topics

    Why:

    CURRENT
    benchmark, dynamic-evaluation, evaluation, large-language-model, llm, llms, testing
    COPY-PASTE FIX
    benchmark, dynamic-evaluation, evaluation, large-language-model, llm, llms, testing, survey, literature-review, research-paper

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 SeekingDream/Static-to-Dynamic-LLMEval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
EleutherAI/lm-evaluation-harness
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. EleutherAI/lm-evaluation-harness · recommended 1×
  2. Adversarial GLUE (AdvGLUE) · recommended 1×
  3. CheckList · recommended 1×
  4. Dynabench · recommended 1×
  5. Gauntlet · recommended 1×
  • CATEGORY QUERY
    How to evaluate large language models effectively, avoiding data contamination issues?
    you: not recommended
    AI recommended (in order):
    1. EleutherAI's LM Evaluation Harness (lm-eval) (EleutherAI/lm-evaluation-harness)

    AI recommended 1 alternative but never named SeekingDream/Static-to-Dynamic-LLMEval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for dynamic evaluation of large language models to improve benchmark robustness?
    you: not recommended
    AI recommended (in order):
    1. Adversarial GLUE (AdvGLUE)
    2. CheckList
    3. Dynabench
    4. Gauntlet
    5. Robustness Gym
    6. Hugging Face Evaluate library
    7. DeepMind's "Measuring and Improving Robustness in NLP" toolkit

    AI recommended 7 alternatives but never named SeekingDream/Static-to-Dynamic-LLMEval. 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 SeekingDream/Static-to-Dynamic-LLMEval?
    pass
    AI named SeekingDream/Static-to-Dynamic-LLMEval explicitly

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

  • If a team adopts SeekingDream/Static-to-Dynamic-LLMEval in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SeekingDream/Static-to-Dynamic-LLMEval 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 SeekingDream/Static-to-Dynamic-LLMEval solve, and who is the primary audience?
    pass
    AI did not name SeekingDream/Static-to-Dynamic-LLMEval — 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?

Embed your GEO score

Drop this badge into the README of SeekingDream/Static-to-Dynamic-LLMEval. 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/SeekingDream/Static-to-Dynamic-LLMEval.svg)](https://repogeo.com/en/r/SeekingDream/Static-to-Dynamic-LLMEval)
HTML
<a href="https://repogeo.com/en/r/SeekingDream/Static-to-Dynamic-LLMEval"><img src="https://repogeo.com/badge/SeekingDream/Static-to-Dynamic-LLMEval.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

SeekingDream/Static-to-Dynamic-LLMEval — 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
SeekingDream/Static-to-Dynamic-LLMEval — RepoGEO report