MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA
Large language models (LLMs) have demonstrated strong reasoning performance, but their tendency to hallucinate limits their reliability in knowledge-intensive tasks requiring up-to-date and grounded information. Combining knowledge graphs (
https://arxiv.org/abs/2607.14561v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
Light
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 ↓ declining
▸ Add / edit details
Correct facts used on the next screening or memo.
People
N
Nikit Srivastava
1.0
low confidence
D
Daniel Vollmers
1.0
low confidence
R
René Speck
1.0
low confidence
N
Nikolaos Karalis
1.0
low confidence
H
Hamada M. Zahera
1.0
low confidence
A
Axel-Cyrille Ngonga Ngomo
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