From Critic to Confidence: PPO for Language-Based Quantitative Prediction with C
LLMs can perform language-based quantitative prediction from unstructured inputs, but remain susceptible to hallucinations and overconfident errors, making it critical to know not only what a model predicts, but when its predictions can be
https://arxiv.org/abs/2607.12687v1 ↗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 ↓ declining
▸ Add / edit details
Correct facts used on the next screening or memo.
People
M
Mehak Dhaliwal
1.0
low confidence
R
Rasta Tadayon
1.0
low confidence
A
Andong Hua
1.0
low confidence
H
Haewon Jeong
1.0
low confidence
Y
Yao Qin
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Automorphic
· Active
Infuse knowledge into language models with just 10 samples
founders not scraped yet