REPOGEO REPORT · LITE

dso-org/deep-symbolic-optimization

Default branch master · commit 8348d5b0 · scanned 6/14/2026, 10:56:51 PM

GitHub: 730 stars · 154 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 dso-org/deep-symbolic-optimization, 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 improve categorization

Why:

COPY-PASTE FIX

deep-learning, symbolic-regression, symbolic-optimization, reinforcement-learning, machine-learning, pytorch, tensorflow

highreadme#2

Strengthen README's opening to highlight unique deep learning approach

Why:

CURRENT

Deep Symbolic Optimization (DSO) is a deep learning framework for symbolic optimization tasks. The package `dso` includes the core symbolic optimization algorithms, as well as support for two particular symbolic optimization tasks: (1) _symbolic regression_ (recovering tractable mathematical expressions from an input dataset) and (2) discovering _symbolic policies_ for reinforcement learning environments.

COPY-PASTE FIX

Deep Symbolic Optimization (DSO) is a state-of-the-art deep learning framework specifically designed for **symbolic optimization tasks**, offering a unique approach to problems like **symbolic regression** (recovering tractable mathematical expressions from data) and discovering **symbolic policies for reinforcement learning environments**. Unlike traditional methods, DSO leverages deep learning to achieve superior performance in these domains.

mediumhomepage#3
Add the repository URL as the project homepage
Why:
COPY-PASTE FIX
```
https://github.com/dso-org/deep-symbolic-optimization
```

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 dso-org/deep-symbolic-optimization

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

AlphaZero/MuZero

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

AlphaZero/MuZero · recommended 1×
TensorFlow · recommended 1×
PyTorch · recommended 1×
DeepProbLog · recommended 1×
Neural Logic Machines · recommended 1×

CATEGORY QUERY
What deep learning frameworks help with discovering symbolic policies for reinforcement learning?
you: not recommended
AI recommended (in order):
1. AlphaZero/MuZero
2. TensorFlow
3. PyTorch
4. DeepProbLog
5. Neural Logic Machines
6. Logic Tensor Networks
7. PyTorch Geometric
8. Deep Graph Library
9. DreamCoder
10. OpenAI Gym
11. OpenAI Baselines
12. Stable Baselines3
13. RLlib
14. Captum
15. TF-Explain
AI recommended 15 alternatives but never named dso-org/deep-symbolic-optimization. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a deep learning library to perform symbolic regression and optimize expressions.
you: not recommended
AI recommended (in order):
1. PySR
2. gplearn
3. DeepMind's AlphaZero
4. TensorFlow/PyTorch
5. Eureqa
AI recommended 5 alternatives but never named dso-org/deep-symbolic-optimization. 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 dso-org/deep-symbolic-optimization?
pass
AI named dso-org/deep-symbolic-optimization explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts dso-org/deep-symbolic-optimization in production, what risks or prerequisites should they evaluate first?
pass
AI named dso-org/deep-symbolic-optimization 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 dso-org/deep-symbolic-optimization solve, and who is the primary audience?
pass
AI did not name dso-org/deep-symbolic-optimization — 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 dso-org/deep-symbolic-optimization. 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/dso-org/deep-symbolic-optimization.svg)](https://repogeo.com/en/r/dso-org/deep-symbolic-optimization)

HTML

<a href="https://repogeo.com/en/r/dso-org/deep-symbolic-optimization"><img src="https://repogeo.com/badge/dso-org/deep-symbolic-optimization.svg" alt="RepoGEO" /></a>

Pro

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dso-org/deep-symbolic-optimization — 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