1/4 What is Core? Understanding Our Own Approach to a Synthetic Brain Architecture Core is not an LLM: Core is not a fine-tuned LLM, not a new LLM, and not an LLM at all. Instead, Core is a multimodal synthetic brain, a fundamentally different type of AI architecture. Key Terminology to Understand Core: 1. Synthetic Brain: Core is a unified cognitive system where multiple AI models and algorithms work as interconnected neural components within a single architecture. Think of it as a digital brain with specialized regions, not a collection of tools. 2. The Bowtie Architecture: Core's memory substrate that stores information as both semantic vectors AND abstract concept nodes, creates connections between seemingly unrelated concepts, and enables genuine concept formation, not just pattern matching. 3. Reasoning Cluster: The cognitive part of Core that orchestrates all thinking processes, making decisions about which neural pathways to activate for any given task, The reasoning cluster is deeply multi-modal and works via parallel processing and sophistication biases.

3/4 Frequently Asked Questions: * “Is Core an advanced LLM?” Core is a multi-modal synthetic brain that uses Language models only for text input/output; it’s not an “advanced LLM.” * “Does Core use AI tooling?” No, Core has integrated AI models as neural components within one synthetic brain. * “Is Core trained on data?” It doesn’t; Core evolves through experience, forming new neural connections. 0.3: Continuous Learning at Inference Time With 0.3, units learn and evolve during every interaction. This continuous learning directly enhances its reasoning abilities: * Forms new neural connections while processing your query: As Core processes new information, it dynamically creates new neural pathways. This means its internal reasoning maps are constantly expanding and adapting, allowing it to connect concepts in novel ways. * Updates its understanding in real-time as concepts emerge: Unlike static models, Core's understanding of concepts isn't fixed. If a new concept is introduced or an existing one is presented in a new context, Core's reasoning adapts immediately, incorporating this new information into its conceptual framework. * Evolves its memory structure through the Bowtie architecture: The Bowtie architecture is not just a storage system; it's a dynamic substrate. As Core learns at inference time, the Bowtie actively reshapes its connections, allowing for more nuanced and sophisticated reasoning by creating and strengthening relationships between ideas. * Develops new conceptual relationships that persist and improve future responses: This is crucial for advanced reasoning. Every interaction allows Core to identify and solidify new relationships between concepts. These persistent relationships mean that Core's reasoning becomes more robust, accurate, and capable of handling complex, unseen scenarios over time, leading to continuously improving future responses. 0.3 enables continuous learning not fine-tuning, not retrieval, but genuine cognitive evolution happening at inference time, directly impacting and refining Core's ability to reason.