All Competitors

A 120M-parameter genomic foundation model that learns adaptive DNA token boundaries via H-Net-style dynamic chunking instead of fixed k-mer or byte-pair tokenization.

A pretrained DNA language model combining Mamba state-space layers, gated dilated convolutions, and Fourier-based attention to capture multi-scale genomic regulatory patterns.

Long-context (64 kb) RNA foundation model using the Striped-Hyena architecture for zero-shot prediction of transcriptome architecture from unspliced pre-mRNA sequence.

A hybrid simulation and machine-learning framework that predicts ribosome location profiles from mRNA sequence alone, combining a structure-aware TASEP with a Mamba polisher.

A long-context plant DNA language model (676M params, Mamba2) pretrained on 65 angiosperm genomes for cross-species functional annotation.

GRN-informed single-cell foundation model combining gene regulatory hierarchy priors with long-sequence Mamba modeling for clustering, batch integration, perturbation modeling, and drug response prediction.

A generative foundation model for biomolecular dynamics that produces atom-level MD-style trajectories for protein monomers and protein-ligand complexes.

Physics-informed generative model that pairs flow matching with a Mamba state-space backbone for linear-time protein backbone design, scaling to 2,000+ residues.

Genomic foundation model for Cypriniformes fish, built on a Mamba-2 state space model with a 32 kb context window for long-range genome modeling.

Generalist EEG-to-text foundation model that translates EEG segments into clinically grounded natural-language descriptions via spectro-spatial grounding and state-space reasoning.

A 2.6B-parameter Liquid Foundation Model fine-tuned via Insilico's MMAI Gym to handle ADMET, retrosynthesis, drug-target activity, and molecular optimization in one checkpoint.

A tokenizer-free, hierarchical autoregressive genomic foundation model that adaptively chunks raw nucleotides, enabling efficient long-context learning and zero-shot variant and gene predictions.

A lightweight plant genome foundation model pairing a bidirectional Mamba backbone with a sparse Mixture-of-Experts, pretrained on 25.4B nucleotides from 42 plant species.

A lightweight pan-cancer single-cell foundation model with a hybrid Transformer-Mamba architecture, released alongside the PanFoMaBench benchmark.

Bidirectional state-space (Mamba-2) genomic model for ultra-long extrachromosomal circular DNA, scaling linearly with sequence length.

A Mamba-based dual-branch UNet that enhances cryo-EM and cryo-ET density maps using local resolution-guided learning to improve interpretability.

Apple's foundation model trained on behavioral signals from wearables, modeling 27 HealthKit metrics to improve predictions across 57 health tasks.

A spatiotemporal foundation model that learns generalizable representations directly from 4D functional MRI volumes for diverse brain-imaging tasks.

A foundational fMRI model that learns brain dynamics via stochastic optimal control, pretrained self-supervised on resting-state scans from 41,072 UK Biobank subjects.

Bidirectional, reverse-complement equivariant DNA language models built on Mamba SSMs. Outperforms models 10x larger on long-range variant effect prediction.