01 Mandate
Pillar Delta AI Labs investigates what intelligence can become when its foundations change—from the neuron and the representation to the memory system and the silicon beneath it.
Our work begins with mechanisms found in biology and asks what they make possible in computation. We build non-monotonic neurons, naturally sparse networks, hierarchical architectures, and systems that reason over time without treating efficiency as an afterthought.
The Lab connects theory to working systems. Ideas move through simulation, implementation, reproducible benchmarks, and hardware-aware testing before they become a platform, a product, or a new research question.
02 Research programs
We work across the intelligence stack because a new architecture cannot be separated from the representation, memory, and compute systems that make it useful.
NmNN replaces the conventional always-rising neuron with a three-region response: silent, peak, and decay. That primitive creates selective activation and networks that are sparse by design.
HyperToken reorganizes language into semantic, structural, and abstraction axes. MeaningMemory explores how an AI can identify, score, and retrieve what matters across time.
Project Aylos and N1 translate structural sparsity into an acceleration layer: active-block routing, block-diagonal compute, fused activation, and a path from software kernels to dedicated silicon.
We test systems whose connectivity, temporal state, or computational substrate can change—from learned dendritic routing to Physarum-inspired networks and four-level logic.
03 Foundational mechanism
A conventional activation mostly knows how to say “more.” The non-monotonic neuron is selective: it stays silent below a threshold, rises to a peak, then decays. The result is a richer computational primitive and exact zeros that the rest of the system can exploit.
Architectural choices begin with documented mechanisms in cortical computation, not biology used as decoration.
Sparsity is part of the computational design itself, so efficiency can remain meaningful as systems scale.
New primitives can extend existing models and hardware while we develop architectures built around them from first principles.
04 Active stack
Each program answers a different part of the same question: how do we build capable systems that compute less, represent more, and retain what matters?
FOUNDATION
The core three-region non-monotonic neuron and the foundation for the architecture family.
NEURAL PRIMITIVEARCHITECTURE
Sparse networks composed into hierarchical superneurons for language, vision, signals, and temporal data.
MULTI-DOMAINREPRESENTATION
A geometric alternative to one-dimensional token sequences, organized by meaning, position, and abstraction.
THEORY V3.0MEMORY
An infrastructure layer for deciding what an AI should remember, how important it is, and when it should return.
PERSISTENT CONTEXTACCELERATION
A hardware-aware sparse compute path from PyTorch and Triton kernels toward a dedicated inference processor.
ROAD TO SILICONFRONTIER
Four-level logic, learnable connectivity, and bio-inspired adaptive routing beyond conventional training assumptions.
EXPERIMENTAL05 From research to production
The Lab is not the final destination. When an architecture survives implementation and verification, we translate it into a system that can operate beyond the experiment.
TRANSLATIONAL SYSTEM
PRODUCTION ENGINEERING · PRE-AUDITCAPSULE TRANSPORT PROTOCOL
CTP is a research-originated secure communication architecture translated into a native implementation and a cross-verified protocol stack. Instead of treating a message as durable data to be decrypted, CTP packages it as a session-bound executable capsule designed to render once and erase itself.
Explore the CTP technical overview06 Research signals
Selected signals from the current research corpus. Validation conditions vary by model and domain.
07 How we work
Start with a mechanism from biology, physics, mathematics, or information theory.
Turn the intuition into a computational primitive, a model, and a falsifiable claim.
Implement the system, test it across scales, and examine where its behavior breaks.
Move validated ideas into research tools, efficient runtimes, products, or silicon.
08 Collaborate
We are opening selected research programs to scientific collaborators, technical partners, and organizations exploring efficient intelligence.
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