Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction
Rubric-based evaluation is a promising approach for assessing open-ended outputs from LLM-based research agents, particularly in paper reproduction, where direct paper-to-repository comparison is prone to hallucination. However, constructin
https://arxiv.org/abs/2607.12835v1 ↗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
agents, llm
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
H
Hanhua Hong
1.0
low confidence
Y
Yizhi Li
1.0
low confidence
J
Jiaoyan Chen
1.0
low confidence
L
Luu Gia Huy
1.0
low confidence
S
Sophia Ananiadou
1.0
low confidence
J
Jung-jae Kim
1.0
low confidence
C
Chenghua Lin
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
PullRequest
· Acquired
Code review as a service - combining automation with a network of…
Lyal Avery
Confident AI
· Active
The LLM Eval and Observability Platform for AI Quality
founders not scraped yet