Investigating first-language bias in LLM-based automated essay scoring: A cross-
This study examines the cross-prompt generalization and first-language (L1) scoring effects of a LoRA-adapted open-weight large language model (Gemma-3-27B-it) applied to automated essay scoring. Using the identical model and inference conf
https://arxiv.org/abs/2607.14605v1 ↗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
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.
Activity & evidence
Similar baseline plays (YC · idea space)
Mimir
· Acquired
A platform for STEM courses featuring automated grading, plagiarism…
Prahasith Veluvolu
Colton Voege
Compose.ai
· Active
Cut your writing time by 40% with AI-powered autocomplete
founders not scraped yet