HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via S
While Large Language Models (LLMs) excel in many general NLP tasks, their formal reasoning capabilities are often compromised by content effects, demonstrating a measurable bias towards real-world plausibility. In this paper, we present our
https://arxiv.org/abs/2607.14349v1 ↗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 ↓ declining
▸ Add / edit details
Correct facts used on the next screening or memo.
People
A
Abdullah Shaikh
1.0
low confidence
Z
Zain Naqi
2.0
low confidence
T
Taha Zahid
2.0
low confidence
S
Sandesh Kumar
2.0
low confidence
A
Abdul Samad
2.0
low confidence
A
A. Y. Shaikh
3.0
low confidence
Activity & evidence
Similar baseline plays (YC · idea space)
Envariant
· Active
Interpretability and reasoning infra for foundation models.
founders not scraped yet
Automorphic
· Active
Infuse knowledge into language models with just 10 samples
founders not scraped yet