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Karthik Viswanathan

3 accepted papers

2026

BioToken and BioFM – Biologically-Informed Tokenization Enables Accurate and Efficient Genomic Foundation Models

ICML 2026poster

Existing genomic foundation models (GFMs) typically treat DNA as raw nucleotide sequences, often overlooking the regulatory context required to interpret genetic variation accurately. We introduce BioToken, a tokenization framework that directly encodes variants and biological annotations into genom…

Cited by 0SourceScholar
2026

Genomic Foundationless Models: Pretraining Does Not Promise Performance

ICLR 2026poster

The success of Large Language Models has inspired the development of Genomic Foundation Models (GFMs) through similar pretraining techniques. However, the relationship between pretraining performance and effectiveness in downstream ge- nomic tasks remains unclear. Additionally, the high computationa…

Cited by 0SourcecodeScholar
2025

Persistent Topological Features in Large Language Models

ICML 2025poster

Understanding the decision-making processes of large language models is critical given their widespread applications. To achieve this, we aim to connect a formal mathematical framework—zigzag persistence from topological data analysis —with practical and easily applicable algorithms. Zigzag persiste…