RRepoGEO

REPOGEO REPORT · LITE

reasoning-machines/pal

Default branch main · commit f81ca2a9 · scanned 6/15/2026, 2:43:04 PM

GitHub: 522 stars · 66 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 reasoning-machines/pal, 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's opening paragraph to clarify PaL's unique approach.

    Why:

    CURRENT
    Repo for the paper PaL: Program-Aided Language Models.
    COPY-PASTE FIX
    PaL (Program-Aided Language Models) is a research framework that enables Large Language Models (LLMs) to solve complex reasoning problems by generating and executing Python code, offloading computation to a program runtime. Unlike general LLM orchestration frameworks, PaL focuses specifically on enhancing LLM accuracy for tasks requiring verifiable, step-by-step computation and procedural logic.
  • hightopics#2
    Add more specific topics to improve categorization.

    Why:

    CURRENT
    commonsense-reasoning, few-shot-learning, language-generation, language-model, large-language-models, reasoning
    COPY-PASTE FIX
    commonsense-reasoning, few-shot-learning, language-generation, language-model, large-language-models, reasoning, code-execution, program-aided-llm, mathematical-reasoning, procedural-tasks
  • mediumcomparison#3
    Add a 'Comparison' section to the README.

    Why:

    COPY-PASTE FIX
    ## Why PaL? How it differs from other approaches

PaL distinguishes itself by focusing on the LLM's ability to *generate and execute verifiable Python code* for complex reasoning, rather than relying solely on natural language chains of thought or general LLM orchestration. While tools like OpenAI's Code Interpreter offer similar execution capabilities, PaL provides a flexible research framework for exploring and implementing program-aided reasoning techniques directly within your LLM applications, offering fine-grained control over the prompting and execution flow.

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 reasoning-machines/pal
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Code Interpreter (formerly Advanced Data Analysis) by OpenAI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Code Interpreter (formerly Advanced Data Analysis) by OpenAI · recommended 1×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. DSPy · recommended 1×
  5. AutoGPT/BabyAGI · recommended 1×
  • CATEGORY QUERY
    How to make language models solve complex reasoning problems using generated code execution?
    you: not recommended
    AI recommended (in order):
    1. Code Interpreter (formerly Advanced Data Analysis) by OpenAI

    AI recommended 1 alternative but never named reasoning-machines/pal. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library to improve LLM accuracy on complex math and procedural challenges.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. DSPy
    4. AutoGPT/BabyAGI
    5. SymPy
    6. NumPy/SciPy

    AI recommended 6 alternatives but never named reasoning-machines/pal. 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 reasoning-machines/pal?
    pass
    AI named reasoning-machines/pal explicitly

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

  • If a team adopts reasoning-machines/pal in production, what risks or prerequisites should they evaluate first?
    pass
    AI named reasoning-machines/pal 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 reasoning-machines/pal solve, and who is the primary audience?
    pass
    AI named reasoning-machines/pal 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 reasoning-machines/pal. 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/reasoning-machines/pal.svg)](https://repogeo.com/en/r/reasoning-machines/pal)
HTML
<a href="https://repogeo.com/en/r/reasoning-machines/pal"><img src="https://repogeo.com/badge/reasoning-machines/pal.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

reasoning-machines/pal — 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