Story

I am a builder, not a researcher. Version 0.6.0 of NEURAL_COVENANT moves beyond standard LLM text prediction into a functional 129-modality AGI implementation.

Repo: https://github.com/AI-Sovereign/Multimodal-AGI-Architecture-...

Key Engineering Specs: * 129-Modality Nexus: Ingests Bio-Physiological (HRV/GSR), Electromagnetic (RF/WiFi), and Semantic data. Handled via Hierarchical Tensor Fusion into a shared latent space Z. * The Neural Trinity: Integrates snntorch (LIF spiking neurons) for event-driven efficiency, torch-geometric (GCN) for relational mapping, and Transformers for high-level cognition. * Performance: Serialized via FlatBuffers and served through a Granian (Rust) backend to maintain sub-millisecond latency. * Mathematical Core: Optimizes for Capacity Φ to maintain intelligence density across streams. Spiking dynamics: U[t+1] = βU[t] + (1-β)I[t] - S[t]U_thresh.

This is a local-first, sovereign architecture. I’m looking for feedback from the community on the normalization efficiency of the 129-modality super-tensor using Polars and the GCN update normalization constants.