FallnAI Research designs uncertainty-aware architectures for scalable Transformers— Mixture-of-Experts, sparse attention, progressive compression, and bidirectional knowledge transfer. Everything is released with code and artifacts.
We work at the intersection of architecture design, sparse computation, and practical research engineering for large models.
Entropy-driven routing, dynamic expert counts, and token-level compute allocation.
Hybrid softmax/sparsemax, graph-constructed patterns, and content-adaptive sparsity.
Token merging with residual pathways for edge and long-context efficiency.
Reciprocal distillation, load balancing, and robustness under distribution shift.
Reproducible pipelines, lightweight prototypes, and open release of code + notebooks.
Full artifacts, fixed seeds, and clear experimental configs for every released project.
Technical reports and research prototypes. Click any card for details.
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FallnAI Research is an independent lab focused on the engineering of efficient, adaptive neural architectures. We study how uncertainty signals—especially predictive entropy—can coordinate sparse computation across attention, routing, and sequence length.
Our approach is research-engineering oriented: small, controllable prototypes that surface clear inductive biases, full open release of code and notebooks, and a preference for methods that transfer to production sparse kernels and long-context settings.
Future projects will continue to explore scalable MoE training, content-adaptive sparsity, edge-friendly compression, and related areas in efficient machine learning.
Founder & Lead Researcher
Adaptive sparse Transformers, entropy-guided routing, reciprocal knowledge transfer in MoE.
justin@fallnai-research.orgCollaboration, questions about the code, or future research directions—reach out.
Domain
fallnai-research.org