Experience Memory Graph: One-Shot Error Correction for Agents
Large Language Model (LLM) agents have shown remarkable capabilities in autonomous decision-making by generating sequential trajectories of states, actions, and observations. However, in complex, long-horizon tasks, these agents frequently
https://arxiv.org/abs/2607.13884v1 ↗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
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 ↓ declining
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Correct facts used on the next screening or memo.
People
W
Wenjun Wang
1.0
low confidence
Y
Yuchen Fang
1.0
low confidence
F
Fengrui Liu
1.0
low confidence
Z
Zibo Liang
1.0
low confidence
K
Kai Zheng
1.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Polymath
· Active
Simulation environments to train & evaluate long-horizon AI agents
founders not scraped yet