Memory-Driven Self-Disclosure and Relational Turning Points: A Longitudinal Mult
As conversational AI systems are designed for repeated use, a central question is how a series of interactions becomes a relationship. We present a longitudinal multimodal study of a memory-augmented conversational agent (24 participants x
https://arxiv.org/abs/2607.14593v1 ↗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
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 ↓ declining
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Correct facts used on the next screening or memo.
People
R
Ryuichi Sumida
2.2
low confidence
M
Mao Saeki
2.0
low confidence
M
Masaki Eguchi
2.0
low confidence
S
Sadahiro Yoshikawa
2.0
low confidence
K
Koji Inoue
2.0
low confidence
T
Tatsuya Kawahara
2.0
low confidence
Y
Yoichi Matsuyama
2.0
low confidence