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C
Carlos Fonseca
IBM Research
B
Brian Belgodere
IBM Research
D
David Cox
IBM Research
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Founder's Pitch
"CliffSearch offers an AI-driven, evolutionary framework that enhances scientific algorithm discovery by ensuring correctness and originality through agent-based mutation and review processes."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
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4/4 signals
Series A Potential
2/4 signals
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Why It Matters
This research provides a more structured and reliable framework for scientific algorithm discovery by incorporating both theoretical and practical elements, reducing the gap between generating proposals and scientifically validating them.
Product Angle
CliffSearch can be productized as a platform-as-a-service for enterprises, particularly R&D departments, that need to develop and validate new computational models efficiently with higher scientific integrity.
Disruption
It replaces current LLM-based discovery systems that lack rigorous correctness and originality checks, offering a more validated and reliable alternative.
Product Opportunity
There's a significant opportunity in the R&D departments of large corporations and research institutions where the validation of complex algorithms is crucial. These entities will pay for increased reliability and reduced time-to-discovery.
Use Case Idea
A commercial platform offering enterprise solutions for algorithm discovery, where businesses can iteratively develop, test, and validate scientific computational models.
Science
CliffSearch uses an evolutionary approach where algorithms evolve based on both their theoretical foundations and code performance. LLM agents play roles in mutation and review, ensuring correctness and originality through reviewer gating and split mutation strategies (exploration vs. correction).
Method & Eval
CliffSearch was illustrated through empirical studies on transformer hyper-connection evolution and optimizer discovery. It demonstrated genuine geometric breakthroughs and maintained a focus on both reproducibility and scientific correctness.
Caveats
There is dependency on LLMs for mutation and review, which may lead to biases or reliability issues in certain domain-specific tasks. Additionally, establishing distribution and adoption within conservative scientific communities can be challenging.
Author Intelligence
Youssef Mroueh
IBM Research
mroueh@us.ibm.com
Carlos Fonseca
IBM Research
Brian Belgodere
IBM Research
David Cox
IBM Research