Benchmarking Multimodal Large Language Models for Scientific Visualization Liter
Multimodal large language models (MLLMs) are increasingly used to interpret visualizations, yet current evaluations remain largely chart-centric and provide limited evidence of understanding of scientific visualization (SciVis). We benchmar
https://arxiv.org/abs/2607.15176v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
None
How close the company’s idea is to your thesis statement
Sector
llm, ai
Overlap with sectors you care about
Geography
Unknown
Location unknown — scores 0
Your thesis: “We back exceptional technical founders building AI-first products and infrastructure, deploying $100K checks within 24 hours.”
Founder → stable
Traction → stable
Idea vs market → stable
▸ Add / edit details
Correct facts used on the next screening or memo.
People
P
Patrick Phuoc Do
1.0
low confidence
C
Chau M. Ta
2.2
low confidence
C
Chaoli Wang
2.0
low confidence
P
Patrick Do
1.8
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
William Wolf
Justin Woodbridge
floatingpoint
· Active
Datasets and evals to push the frontier of visual intelligence.
founders not scraped yet