Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Ev
Rubrics provide structured, fine-grained signals for training and evaluating large language models (LLMs). Yet reliable query-specific rubrics are difficult to construct. Existing approaches often derive supervision from human-written rubri
https://arxiv.org/abs/2607.15092v1 ↗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
H
Haocheng Yang
1.0
low confidence
L
Licheng Pan
1.0
low confidence
X
Xiaoxi Li
1.0
low confidence
Z
Zhichao Chen
1.0
low confidence
Z
Zhiheng Zhang
1.0
low confidence
Y
Yuan Lu
1.0
low confidence
H
Hao Wang
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Chris Osborn
Automorphic
· Active
Infuse knowledge into language models with just 10 samples
founders not scraped yet