Foundational research

We develop novel approaches for underexplored data modalities where custom architectures still outperform generic solutions. When a domain awaits its breakthrough moment, we build from first principles rather than forcing ill-fitted models.

State of the art techniques

We don't just apply these techniques, we extend them. Our team publishes, experiments, and pushes these methods into new territory, then deploys what works.

Attention mechanisms

SSM / Mamba

Flow matching & diffusion

Reinforcement Learning

Representation Learning

Graph Neural Networks

# research.lab

switch PROJECT {

apply( <<reinforcement_learning>>)

apply( <<flow_matching>>)

apply( <<mamba>>)

}

# optimize.run

model {

prune( <<weights>>)

quantize( <<int8/int4>>)

distill( <<student_model>>)

}

Architecture optimization

Advanced optimization techniques transform resource-intense models into practical deployments, maintaining accuracy while dramatically reducing computational requirements.

Pruning

Quantization

Distillation

Adaptive computing

Hardware-aware design

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