Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Inte
As Code Large Language Models (LLMs) become central to modern software engineering, their inherent stochasticity poses significant real-world risks, where even minor errors can lead to severe functional, security, or safety consequences. Re
https://arxiv.org/abs/2607.12273v1 ↗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 ↑ improving
▸ Add / edit details
Correct facts used on the next screening or memo.
People
X
Xiaoning Ren
1.0
low confidence
Y
Yinxing Xue
1.0
low confidence
L
Lei Ma
1.0
low confidence
Y
Yuheng Huang
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Airtrain AI
· Inactive
No-code data curation for LLM fine-tuning and evaluation.
founders not scraped yet