Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation
Multimodal Large Language Models (MLLMs) are increasingly deployed for nuanced content safety and moderation tasks, yet they remain vulnerable to adversarial attacks and out-of-distribution edge cases. Traditional active learning and manual
https://arxiv.org/abs/2607.14256v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
Light
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 ↓ declining
▸ Add / edit details
Correct facts used on the next screening or memo.
People
G
Genglin Liu
2.6
low confidence
M
Muye Zhang
2.0
low confidence
K
Krishnamurthy Viswanathan
1.0
low confidence
N
Nichole J. Hansen
2.0
low confidence
B
Blaž Bratanič
2.0
low confidence
N
Nathan L Clement
1.0
low confidence
S
Shalini Ghosh
2.0
low confidence
A
Ariel Fuxman
2.0
low confidence
K
K. S. Viswanathan
1.0
low confidence
N
Nathan Clement
1.0
low confidence
Activity & evidence
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