RRepoGEO

REPOGEO REPORT · LITE

callous-youth/BOAT

Default branch main · commit 9f58f70c · scanned 6/23/2026, 12:03:46 AM

GitHub: 1,062 stars · 151 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 callous-youth/BOAT, 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:

    CURRENT
    (none)
    COPY-PASTE FIX
    bi-level-optimization, gradient-based-optimization, machine-learning, deep-learning, pytorch, optimization-framework, compositional-ai, solver-variants
  • highreadme#2
    Add a clear, descriptive H1 to the README

    Why:

    CURRENT
    <h1 align="center"></h1>
    COPY-PASTE FIX
    <h1>BOAT: A Compositional Operation Toolbox for Gradient-based Bi-Level Optimization in PyTorch</h1>
  • mediumreadme#3
    Strengthen the README's opening paragraph with a clear problem statement and differentiator

    Why:

    CURRENT
    BOAT (Operation-level Toolbox for gradient-based BLO) is a compositional, operation-level framework designed to bridge the gap between theoretical modeling and practical implementation in Bi-Level Optimization (BLO). Unlike existing libraries that typically encapsulate fixed solver routines, BOAT factorizes the BLO workflow into atomic, reusable primitives. Through a unified constraint reconstruction perspective, it empowers researchers to automatically compose over 85+ solver variants from a compact set of 19 gradient operations.
    COPY-PASTE FIX
    BOAT (Operation-level Toolbox for gradient-based BLO) is a compositional, operation-level framework designed to bridge the gap between theoretical modeling and practical implementation in Bi-Level Optimization (BLO). It addresses the limitation of existing libraries that offer only fixed solver routines by factorizing the BLO workflow into atomic, reusable primitives. Through a unified constraint reconstruction perspective, BOAT empowers researchers to automatically compose over 85+ solver variants from a compact set of 19 gradient operations, offering unprecedented flexibility and control.

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 callous-youth/BOAT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Julia
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Julia · recommended 2×
  2. CasADi · recommended 2×
  3. MATLAB · recommended 2×
  4. JAX · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    How to implement gradient-based bi-level optimization with a compositional approach?
    you: not recommended
    AI recommended (in order):
    1. JAX
    2. PyTorch
    3. TensorFlow
    4. Keras
    5. tf.function
    6. Julia
    7. Zygote.jl
    8. ChainRules.jl
    9. CasADi
    10. SciPy
    11. MATLAB
    12. Symbolic Math Toolbox
    13. Deep Learning Toolbox

    AI recommended 13 alternatives but never named callous-youth/BOAT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a flexible framework for bi-level optimization beyond fixed solver routines.
    you: not recommended
    AI recommended (in order):
    1. Pyomo
    2. GAMS (General Algebraic Modeling System)
    3. Julia
    4. JuMP.jl
    5. CasADi
    6. MATLAB
    7. Optimization Toolbox
    8. YALMIP
    9. SciPy.optimize

    AI recommended 9 alternatives but never named callous-youth/BOAT. 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 callous-youth/BOAT?
    pass
    AI named callous-youth/BOAT explicitly

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

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

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

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callous-youth/BOAT — 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