Poster Wed, Jul 8, 2026 • 6:30 PM – 8:15 PM PDT HALL A #2814

Scaling Laws and Architectural Frontiers in Metagenomic Foundation Models

Geraldene Munsamy ⋅ Gavin Ayres ⋅ Jérémie DONA ⋅ Carla Greco ⋅ Daniel P Anderson ⋅ Srijani Sridhar ⋅ William Chow ⋅ Aaron Kollasch ⋅ Robert Pecoraro ⋅ Tanggis Bohnuud ⋅ Keith Kam ⋅ Gus Minto-Cowcher ⋅ Marcus Leung ⋅ Hassan Sirelkhatim ⋅ John St. John ⋅ Ali Taghibakhshi ⋅ Tyler Shimko ⋅ Jared Wilber ⋅ Timur Rvachov ⋅ Saee Paliwal ⋅ Eddie Calleja ⋅ Noelia Ferruz ⋅ Kevin Yang ⋅ Francesco Farina ⋅ Philipp Lorenz

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Abstract

Foundation models for genomics have the potential to revolutionize therapeutic design, yet the optimal architectural choices for modeling the vast and diverse distribution of metagenomic data remain under-explored. In this work, we present the machine learning methodology behind MODEL, a family of metagenomic foundation models scaled up to 28 billion parameters and trained on 9.7 trillion nucleotide tokens. We provide a systematic empirical study of architectural trade-offs between autoregressive Transformers (Llama-style), State-Space Models (Mamba), and Long-convolutional architectures (Hyena) for nucleotide-level modeling. Contrary to recent trends favoring linear-time sequence models for long-range biological data, we demonstrate that the Llama architecture exhibits superior scaling efficiency and semantic retrieval capabilities as the model capacity grows. We derive a set of quality-aware scaling laws for metagenomics, showing how model performance follows predictable power-law behavior across three orders of magnitude in parameters and data. Through extensive benchmarking, spanning unsupervised zero-shot fitness prediction, semantic completion, and gene recovery, we establish a blueprint for scaling biological foundation models and provide empirical evidence demonstrating why Transformer-based architectures define the current frontier.