Paradigm-different from neural networks. Biology-faithful. Formally verified. Edge-deployable. No cloud infrastructure dependency.
USRA — Universal Self Reading Architecture · Patent applications pending
inclusiv.ai is a private research programme developing Universal Self Reading Architecture (USRA) — a paradigm-different cognitive architecture derived from mathematical first principles, not from neuro-mimicry approaches.
We develop cognitive systems that learn through procedural curricula grounded in developmental psychology (Vygotsky, Piaget, Bloom), with orders of magnitude lower training corpus requirements than foundation models. Our architectures do not use neural networks, weights, gradient descent, or stochastic sampling.
The approach is architectural and structural, not statistical. The structural foundation is primary; biology-faithfulness emerges from mathematical derivation. The structural layer is formally verified through the Lean4 proof assistant. Execution is byte-identical — deterministic as an architectural property, not as stochastic approximation.
A world where AI does not depend on extractive infrastructure, does not require billions of parameters, does not waste resources on simple tasks, and does not represent a systemic threat to privacy or democratic pluralism.
Cognitive systems that grow with the user, on-device, lifelong — math tutors, learning companions, translators for low-resource languages, physical embodiments for real-world interaction.
To develop, formally verify, and empirically demonstrate a paradigm-different cognitive architecture, and to secure IP protection so that the technology becomes available through responsible commercial and research channels.
Special focus: education for marginalised communities, access to AI-supported pedagogy for low-resource languages, and ethical integration into critical systems (healthcare, finance, public services).
Foundation models (GPT, Claude, Gemini, LLaMA, and other LLM architectures) are technologically impressive but structurally cannot serve certain contexts. Some problems cannot be solved by larger models — only by different architectures.
Foundation models require billions of examples for basic capabilities. A child learns mathematics from a few dozen examples. Difference: orders of magnitude.
The largest models require cloud infrastructure. Privacy is an opt-in privilege, not an architectural property. On-device deployment is a workaround, not a design.
Foundation models give different answers to the same question. Scientific replicability and regulatory certifiability are structurally harder to achieve.
Training and inference of foundation models consume enormous amounts of electricity, water, rare elements, and human labour. AI today is a physical extractive industry.
Foundation models forget previously learned content when learning new tasks. Lifelong learning with the same model is structurally not possible.
The social layer with the least information about AI shows the highest trust. Structural vulnerability of democratic societies to AI influence systems.
Our response: these are not technical limitations to be solved by larger models. They are architectural consequences of one approach — the neural network paradigm. USRA approaches cognitive systems differently.
Educational materials about the AI domain — from history of approaches, through today's paradigms, to emerging models. No marketing spin, scientifically grounded.
"AI" is an umbrella term covering several paradigms that differ significantly. Formally recognised categories in the 2026 academic taxonomy:
"AI" in public discussion typically means "deep learning + LLM" — that is the currently dominant paradigm, not the only one.
USRA does not belong to any of the above categories. It is being developed as a new approach that derives cognitive structure from mathematical first principles — as a category in academic taxonomy it does not yet formally exist. This is intentional: "paradigm-different" means precisely that it does not fit established frames and requires its own categorisation.
A neural network is a mathematical structure inspired by biological neurons:
This paradigm has been dominant since 2012 (AlexNet) and especially since 2017 (Transformer architecture). LLMs (GPT, Claude, Gemini, LLaMA) are transformers with billions of weights trained on petabytes of text.
The Universal Approximation Theorem mathematically proves that a neural network with enough nodes can approximate any continuous function. This is why scaling works. But approximation is not the same as understanding.
Five post-transformer alternatives currently under active research (all remain within the neural network paradigm):
All of these are neural networks — they use weights, activations, gradient descent or surrogate gradients, and depend on training corpora.
USRA belongs to a categorically different class — first-principles cognitive architecture. Not a neural network. Different computational primitives, different learning mechanisms, different architectural guarantees.
What we use today and what is on our wishlist — transparent about the technical infrastructure supporting our research.
Hardware support or sponsorship welcome through partnership programmes. Contact: Engage with us.
Our approach to AI is simultaneously technically specialised and pedagogically integrated. Educational materials for all levels — from primary school students to researchers.
What AI is, how to distinguish paradigms, what foundation models can and cannot do, how to approach AI tools safely and critically.
In preparation: a series of educational materials for primary school students, secondary school students, and adults without technical background.
Technical materials for STEM students — mathematical foundations of cognitive architectures, mathematical structures in AI, formal verification (Lean4), developmental psychology applied to AI training.
In preparation: technical blog posts, mini-tutorials, open-source examples.
Our approach to teaching cognitive systems is rooted in the heritage of developmental psychology — Vygotsky's Zone of Proximal Development, Piaget's developmental stages, Bloom's taxonomy of learning, Montessori and Reggio Emilia pedagogical approaches. We treat the AI system as a learner passing through developmental stages, not as a tool trained on a massive corpus.
What we are testing in 2026, described accessibly. Detailed empirical results and the specific mechanisms enabling these properties are protected by patent process; they will be progressively published as patent priority is established.
Our formally-verified developmental hierarchy spans cognitive tiers from the simplest cell to human cognitive complexity. Currently active testing is at L15 (sea slug). Higher tier configurations are structurally supported but require more powerful hardware — current testing reach is limited by available resources, not by architectural limits. Empirical validation of higher tiers awaits access to adequate compute infrastructure (see Hardware section).
| Tier range | Biological complexity reference | Research status |
|---|---|---|
| L1 – L4 | Earliest developmental stages (zygote, morula, blastula, gastrula) | Structurally verified |
| L5 – L9 | Embryonic development, first primitive cognitive primitives | Structurally verified |
| L10 – L14 | Simple invertebrates, first cognitive systems | Structurally verified |
| L15 | Sea slug (Aplysia californica) | ⚡ Currently active testing |
| L16 – L19 | Insects and simple fish | Roadmap (workstation tier) |
| L20 – L24 | Amphibians, smaller reptiles | Roadmap (cloud-scale tier) |
| L25 – L29 | Small mammals (rodent class) | Roadmap |
| L30 – L34 | Larger mammals (carnivores, primates) | Roadmap |
| L35 | Human cognitive complexity — reference | Structural target of hierarchy |
Tier labels (L1–L35) are our internal developmental levels. Mappings to biological organisms are illustrative comparisons of cognitive complexity, not claims of anatomical equivalence. Detailed node counts, mechanisms, and per-tier empirical results are protected by patent process.
Public materials — visions, architectural framework, pedagogical approach, biological taxonomy. Detailed technical specs and empirical results are protected by patent process; they will be progressively published as IP protection is secured.
VISION
Cognitive architecture derived from mathematical first principles — vision and strategic context.
Status: publicly available
ARCHITECTURE
High-level functional description of the architecture, layer organisation, integration with hardware.
Status: in preparation for public release (post-patent priority)
INDUSTRY
Systematic review of problems with the foundation AI paradigm, perspectives for an alternative architecture.
Status: publicly available
BIOLOGY
Developmental staging framework — from Aplysia californica composability tier to K-12-ready brain configurations.
Status: publicly available (tier specifics NDA-protected)
PEDAGOGY
Developmental psychology applied to AI training — Vygotsky / Piaget / Bloom in the context of cognitive architectures.
Status: publicly available
BENCHMARKS
Detailed empirical results — cohort experiments, autonomous procedural composition, byte-identical reproducibility.
Status: in preparation for public release (post-patent priority)
All public publications have undergone NDA discipline review — disclosing what does not compromise patent IP, reserving mechanisms and specific implementation details for post-NDA technical conversations.
We are open to conversations with research partners, strategic investors, hardware sponsors, and institutions interested in a paradigm-different approach to AI.
We are looking for strategic partners in: educational technology (K-12 tutoring), translation platforms (low-resource languages), embodied AI (robotics, IoT), and hardware ecosystems (Apple, Nvidia, Intel, IBM).
Access under NDA. Patent specification available after signed NDA.
Pre-funding stage. Open to conversations with investors who understand paradigm-different research timelines and deep-tech hardware co-design opportunities.
Materials package available after initial conversation + NDA.
Academic institutions, research groups and independent researchers interested in first-principles cognitive architectures, formal verification of AI, or biology-faithful approaches — contact for research collaboration.
Open to co-authorship, joint publications, replication studies under NDA framework.