This is a conceptual research proposal published by Landn™.
We are not presenting a working implementation. We are presenting an architectural idea and opening it as a research question. Pushback, collaboration, and academic engagement are welcome.
Abstract
Since 1945, every computer ever built has been founded on the von Neumann architecture: a binary logic system in which all information is encoded as sequences of ones and zeros, processed by a central unit, stored in addressable memory, and executed as sequential instructions.
This architecture was designed for electrical switches. Not for human beings.
The result is a permanent translation problem. Humans must learn to express their intentions in a language the machine understands, rather than the machine understanding the language the human already speaks.
This post proposes BRAIN, Base Reasoning Architecture for Intelligent Navigation, as a conceptual alternative to the von Neumann model. BRAIN is a computing architecture in which intent is a primitive operation, ambiguity is a native state, context is a first class citizen, and the machine navigates toward meaning rather than executing fixed binary instructions.
We are not claiming to have solved the physical implementation of BRAIN. We are claiming the architectural principle is sound, the problem it addresses is real, and the scientific community should treat it as an open and urgent research question.
The Problem with the Foundation
The von Neumann architecture rests on four components: a processing unit, a control unit, memory, and input and output mechanisms. All data and all instructions are encoded in binary. The machine processes instructions sequentially. The human must express every intention in terms the machine can parse.
This was not a flaw in von Neumann's thinking. It was a practical constraint of the materials available in the 1940s. Transistors behave as switches: they are either on or off. Binary maps perfectly onto that physical reality.
The problem it was not solving was human communication.
Human language is not binary. Human intent is not sequential. When a person says "send this to the team," they do not specify a file path, a protocol, a recipient list, or a delivery mechanism. They express an intention. The meaning is clear to another human. It is opaque to a von Neumann machine.
The entire history of human computer interaction is the history of attempts to bridge this gap without changing the foundation. Command line interfaces required humans to learn precise syntax. Graphical interfaces translated human actions into machine events through visual metaphors. Voice interfaces converted speech to text and text to commands. Each approach narrowed the gap. None closed it. None could, because the gap is not an interface problem. It is an architectural problem.
What BRAIN Proposes
BRAIN proposes that computing architecture should be redesigned from the principle up, starting not with the question of what materials can do, but with the question of what humans need.
The central claim is this: intent should be a primitive operation in a computing architecture, not a high level abstraction built on top of binary logic.
In the von Neumann model, the primitive operations are binary logic gates: AND, OR, NOT, and their combinations. BRAIN proposes a different set of primitives, chosen because they map onto human cognitive behaviour:
Intent — the expression of a desired outcome without specification of the mechanism
Navigation — the process of finding the most likely path from an expressed intent to an executable outcome, given available context
Context — the accumulated state of prior interactions, environmental conditions, and user patterns that informs how an intent should be interpreted
Resolution — the selection of a specific action from a navigated set of possibilities
A BRAIN processor does not ask "what is the binary state of this input?" It asks "what does this input intend, and what is the most navigable path to fulfilling it?"
How BRAIN Differs from Existing Approaches
BRAIN is not a large language model. It is not a neural network. It is not an AI system layered on top of von Neumann hardware. It is an architectural proposal for the hardware layer itself.
Large language models operate at the software level on von Neumann hardware. They are extraordinarily capable translation machines. But they are still translating. The binary foundation is still there. The transistors are still switching. The chain from human intent to machine execution still passes through binary at every step.
BRAIN proposes that the chain should not exist at the hardware level. The processor itself should operate on intent, navigate toward resolution, and maintain context as a hardware state, not a software abstraction.
Neuromorphic computing, such as Intel's Loihi and IBM's TrueNorth, moves in a related direction by designing processors that mimic neural firing patterns. BRAIN shares the spirit of this work but proposes a different primitive: not neural patterns, but intent and navigation. Quantum computing introduces superposition and entanglement as hardware primitives, breaking the binary constraint in a different direction. BRAIN does not require quantum mechanics. It requires a rethinking of what a processor is designed to compute.
Software as a Proof of Principle
At Landn, we are exploring software implementations of the BRAIN principle through our work on Larca™. These experiments demonstrate that intent navigation is achievable at the software level today, and strengthen the case that it should be pursued at the hardware level tomorrow.
The research question BRAIN poses is: what does this look like when the hardware is built for it from the start? What happens to performance, accessibility, energy efficiency, and capability when intent navigation is a hardware primitive rather than a software abstraction on top of binary logic?
Open Research Questions
We are publishing this not as a completed solution but as an invitation. The following questions define the open research frontier of the BRAIN architecture.
Physical substrate — what material or physical phenomenon could serve as the substrate for intent-native computation? Binary logic maps onto transistor switching. What physical behaviour maps onto intent navigation? Candidates worth exploring include probabilistic computing substrates, biological or molecular computing, and photonic systems capable of representing continuous rather than discrete states.
Formal specification of intent — how is intent formally represented at the hardware level? Binary has a complete mathematical foundation in Boolean algebra. BRAIN requires an equivalent: a formal mathematical system for representing intent, ambiguity, context, and resolution.
Primitive instruction set — von Neumann machines have an instruction set architecture that defines the operations a processor can perform. What is the equivalent instruction set for a BRAIN processor?
Interoperability — how do BRAIN and von Neumann systems communicate?
Energy model — can BRAIN be implemented in a way that is competitive with binary systems on energy consumption?
Why This Matters
Computing has spent eighty years becoming more accessible by building better translation layers. GUIs, touchscreens, voice interfaces, and now large language models have each reduced the burden on the human without changing the foundation. The progress is real. The ceiling is also real.
The people who cannot use technology today are not failing because the interface is bad. They are failing because the gap, however narrowed, still exists. A child in rural Ghana who cannot read cannot navigate a graphical interface. An elderly person unfamiliar with software conventions cannot use a touchscreen application without assistance. A professional who knows exactly what they need but cannot express it in the vocabulary of the software in front of them is stopped by the gap.
BRAIN is built on the belief that the gap should not exist. That technology designed for human beings should begin with human beings, not with transistors. That the eighty year project of bridging the gap should give way to a new project: eliminating it.
Conclusion
We propose BRAIN, Base Reasoning Architecture for Intelligent Navigation, as a formal research direction for post-binary computing architecture. We identify intent, navigation, context, and resolution as the candidate primitives for a human-native computing layer. We present our software work at Landn as a demonstration that the principle is sound. And we open five foundational research questions for the community to engage with.
We do not know what BRAIN looks like in silicon. We know why it should exist. And we know that the work of imagining it, specifying it, and eventually building it is the most important work in computing that nobody is currently doing.
We are starting it here.
Read the full paper at Landn™
Published by Landn™
Top comments (0)