Self-Evolving Agent Harnesses via Gated Semantic Quality-Diversity
An LLM agent's real-task performance is shaped as much by the harness around its model as by the frozen model itself: its prompts, injected knowledge, runtime control, and configuration. In deployment the harness is often the only lever ava
https://arxiv.org/abs/2607.13683v1 ↗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
X
Xiaotian Luo
1.0
low confidence
F
Fengxingyu Wang
1.0
low confidence
C
Chuanrui Hu
1.0
low confidence
D
Dizhan Xue
1.0
low confidence
Y
Yafeng Deng
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
ReasonBlocks
· Active
The runtime layer that makes AI agents cheaper and more reliable
founders not scraped yet