SEMA: Exploring a Different AI Architecture

SEMA is an experimental AI architecture that is not transformer-based and not an LLM, designed to explore language understanding through explicit semantic structure rather than next-token prediction. The project, created by an independent researcher, prioritizes CPU-first design, multilingual capability, transparent internal representations, and graph-based reasoning, with source-available code to encourage independent research and architectural diversity in AI.

I believe AI should remain transparent, inspectable, and accessible to independent researchers—not only to organizations with massive compute budgets. SEMA is motivated by the idea that architectural diversity matters: AI should support local execution, data sovereignty, explicit reasoning, and source availability, ensuring that research remains reproducible, understandable, and independent of centralized infrastructure. I also believe that AI should not become synonymous with a single architectural paradigm. The extraordinary success of LLMs should inspire further exploration, not diminish investment in alternative approaches. A healthy scientific ecosystem depends on architectural diversity, where fundamentally different ideas continue to be explored, challenged, and improved alongside the dominant paradigm. I’ve been working on SEMA, an experimental AI architecture that explores a direction fundamentally different from transformer-based LLMs. The goal is not to build a smaller LLM. The goal is to investigate whether language understanding and reasoning can emerge from explicit semantic structure instead of next-token prediction. Some characteristics of SEMA: Not transformer-based. Not an LLM. CPU-first design. Multilingual by construction. Transparent internal representations instead of opaque embeddings. Explicit relational memory rather than storing knowledge only in neural weights. Composable semantic representations using Vector Symbolic Architecture VSA/HDC . Graph-based reasoning instead of autoregressive generation. One design decision I find particularly interesting is separating semantic representation from knowledge storage. Instead of trying to encode everything into dense neural parameters, SEMA stores explicit relational knowledge and uses high-dimensional semantic representations as an indexing and reasoning mechanism over that knowledge. This makes it possible to inspect structures, analyze intermediate representations, and experiment with reasoning algorithms directly. The project is source available because I want researchers to study, reproduce, and challenge the architecture while protecting the continuity of independent research. I’m not claiming this approach is superior to LLMs. I’m asking a different question: Are there still unexplored architectural directions that deserve serious research alongside the transformer ecosystem? I’d greatly appreciate feedback from anyone interested in alternative AI architectures, VSA/HDC, cognitive systems, graph reasoning, or symbolic approaches.