Semantic Dynamic Grounding Engine (SDGE)

SDGE (Semantic Dynamic Grounding Engine) is a dynamic semantic grounding engine built on a spiking neural network (SNN). It treats meaning and understanding as a physical state inside the network, rather than as probabilistic computation over symbols. The Core Idea (Simplified) Instead of ‘learning’ by sculpting a probability distribution and performing dense weight updates, SDGE follows a brain-like principle: 1. It begins from a rich, chaotic state space (analogous to cortex generating many possible trajectories). 2. It receives a temporal input stream that drives the network along different trajectories. 3. When sufficient structure emerges, the dynamics collapse into a stable attractor. This collapse is the moment of understanding. 4. At that moment the engine commits at t* and captures a triple: Captured at commitment: · h*: the internal ‘imprint of understanding’ (a committed state snapshot). · sigma_hat*: calibrated confidence. · t*: commitment/understanding time. The system then ‘learns’ by storing this imprint as an explicit append-only binding in memory (GroundingBank), rather than by imprinting the knowledge into dense weights. Why This Yields Exceptional Advantages 1) High Data Efficiency (Few-shot) Because learning is not ‘reshaping weights’ to approximate meaning, but reaching a stable understanding state h* and binding it, the engine can add a new concept from very few examples. When the dynamics can form a clear attractor, a new concept can be acquired from a single example (1-shot). 2) Structural Non-Forgetting (Zero Forgetting) In statistical systems, adding new knowledge typically forces updates to shared weights, causing interference and forgetting. In SDGE, new knowledge is appended as bindings in an append-only memory, while old knowledge is not overwritten. Therefore, non-forgetting is structurally implied by the design. 3) A Principled Path to Energy Efficiency SDGE has two physical reasons to be more energy-efficient in principle: · Event-driven SNN computation: most computation occurs on spike events rather than dense matrix multiplications at every step. · Early stop by commitment: the engine effectively stops processing at t* (it does not keep computing after it understands). 4) Understanding Becomes a Measurable, Verifiable Event The triple (t*, h*, sigma_hat*) means SDGE does not merely output an ‘answer’. It provides operational evidence of understanding: · when the system understood (t*), · which internal state expressed that understanding (h*), · and how confident it was, in a calibratable way (sigma_hat*). This makes it possible to produce reproducible operational certificates (artifacts) rather than relying on a black-box claim.