REPOGEO REPORT · LITE

facebookresearch/theseus

Default branch main · commit c8583de4 · scanned 5/17/2026, 3:46:56 PM

GitHub: 2,028 stars · 147 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 facebookresearch/theseus, 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

highabout#1

Clarify About description to highlight PyTorch and differentiable layers

Why:

CURRENT

A library for differentiable nonlinear optimization

COPY-PASTE FIX

A PyTorch library for building custom differentiable nonlinear optimization layers, enabling end-to-end differentiable architectures in robotics and vision.

highhomepage#2
Add the project's homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://sites.google.com/view/theseus-ai/
```

mediumreadme#3

Add a sentence to the README's first paragraph explicitly differentiating Theseus

Why:

CURRENT

Theseus is an efficient application-agnostic library for building custom nonlinear optimization layers in PyTorch to support constructing various problems in robotics and vision as end-to-end differentiable architectures.

COPY-PASTE FIX

Theseus is an efficient application-agnostic library for building custom nonlinear optimization layers in PyTorch to support constructing various problems in robotics and vision as end-to-end differentiable architectures. Unlike generic PyTorch optimizers, Theseus focuses on constructing custom differentiable nonlinear optimization layers, and unlike traditional solvers like Ceres, it is fully integrated with PyTorch for end-to-end differentiability.

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 facebookresearch/theseus

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

PyTorch's Autograd with `torch.optim`

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

PyTorch's Autograd with `torch.optim` · recommended 1×
`torch.optim.Adam` · recommended 1×
`torch.optim.SGD` · recommended 1×
`torch.optim.LBFGS` · recommended 1×
`torch_optimizer` · recommended 1×

CATEGORY QUERY
How can I perform differentiable nonlinear least squares optimization efficiently in PyTorch?
you: not recommended
AI recommended (in order):
1. PyTorch's Autograd with `torch.optim`
2. `torch.optim.Adam`
3. `torch.optim.SGD`
4. `torch.optim.LBFGS`
5. `torch_optimizer`
6. `torch_optimizer.AdaBelief`
7. `torch_optimizer.RAdam`
8. `scipy.optimize.least_squares`
9. `torch_scatter`
10. `optax`
AI recommended 10 alternatives but never named facebookresearch/theseus. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What libraries are available for robust Levenberg-Marquardt optimization in robotics applications?
you: not recommended
AI recommended (in order):
1. Ceres Solver
2. GTSAM
3. Eigen
4. SciPy
5. g2o
6. NLopt
AI recommended 6 alternatives but never named facebookresearch/theseus. 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 facebookresearch/theseus?
pass
AI named facebookresearch/theseus explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts facebookresearch/theseus in production, what risks or prerequisites should they evaluate first?
pass
AI named facebookresearch/theseus 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 facebookresearch/theseus solve, and who is the primary audience?
pass
AI named facebookresearch/theseus explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

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Pro

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facebookresearch/theseus — 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