REPOGEO REPORT · LITE

AGI-Arena/MARS

Default branch main · commit 4831e28e · scanned 6/8/2026, 11:03:06 AM

GitHub: 721 stars · 49 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 AGI-Arena/MARS, 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 project type in README's first sentence to counter miscategorization

Why:

CURRENT

This repository contains the official code for the paper MARS: Unleashing the Power of Variance Reduction for Training Large Models.

COPY-PASTE FIX

This repository presents **MARS**, a novel **optimization framework** (not a multi-agent simulation platform) designed to unleash the power of variance reduction for training large models, specifically addressing challenges in pretraining and fine-tuning large language models.

mediumtopics#2

Add more specific optimization-related topics

Why:

CURRENT

fine-tuning, large-language-models, optimization-algorithms, optimizer, pretraining

COPY-PASTE FIX

fine-tuning, large-language-models, optimization-algorithms, optimizer, pretraining, gradient-descent, deep-learning-optimizer, variance-reduction, llm-training

lowabout#3

Refine GitHub 'About' description for clearer categorization

Why:

CURRENT

The official implementation of MARS: Unleashing the Power of Variance Reduction for Training Large Models

COPY-PASTE FIX

An **optimization framework** (MARS) for training large models, focusing on variance reduction in pretraining and fine-tuning large language models.

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 AGI-Arena/MARS

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

pytorch/pytorch

Recommended in 5 of 2 queries

COMPETITOR LEADERBOARD

pytorch/pytorch · recommended 5×
tensorflow/tensorflow · recommended 4×
huggingface/transformers · recommended 3×
AdamW · recommended 2×
AdaFactor · recommended 2×

CATEGORY QUERY
How to reduce stochastic gradient variance when training large language models effectively?
you: not recommended
AI recommended (in order):
1. PyTorch (pytorch/pytorch)
2. DataLoader (pytorch/pytorch)
3. tf.data.Dataset (tensorflow/tensorflow)
4. AdamW
5. AdaFactor
6. LAMB
7. torch.nn.utils.clip_grad_norm_ (pytorch/pytorch)
8. tf.clip_by_global_norm (tensorflow/tensorflow)
9. Hugging Face transformers (huggingface/transformers)
10. get_linear_schedule_with_warmup (huggingface/transformers)
11. get_cosine_schedule_with_warmup (huggingface/transformers)
12. torch.cuda.amp (pytorch/pytorch)
13. tf.keras.mixed_precision (tensorflow/tensorflow)
14. torch.nn.parallel.DistributedDataParallel (pytorch/pytorch)
15. tf.distribute.MirroredStrategy (tensorflow/tensorflow)
AI recommended 15 alternatives but never named AGI-Arena/MARS. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are robust optimization techniques for pretraining and fine-tuning very large neural networks?
you: not recommended
AI recommended (in order):
1. AdamW
2. AdaFactor
3. Lion
4. SGD with Momentum
5. LAMB
6. Sophia
AI recommended 6 alternatives but never named AGI-Arena/MARS. 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 AGI-Arena/MARS?
pass
AI named AGI-Arena/MARS explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts AGI-Arena/MARS in production, what risks or prerequisites should they evaluate first?
pass
AI named AGI-Arena/MARS 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 AGI-Arena/MARS solve, and who is the primary audience?
pass
AI named AGI-Arena/MARS 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 AGI-Arena/MARS. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/AGI-Arena/MARS.svg)](https://repogeo.com/en/r/AGI-Arena/MARS)

HTML

<a href="https://repogeo.com/en/r/AGI-Arena/MARS"><img src="https://repogeo.com/badge/AGI-Arena/MARS.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite