ADAM-TPU: A Sovereign Complex-Spectral Lattice for Cognitively-Immune LLM Inference and Training via Radial Coalescence

This paper presents an architectural proposal for ADAM-TPU, a non–von Neumann tensorial core for Large Language Model (LLM) inference and constrained on-device adaptation, built on an analog memristive–photonic substrate operating natively in the complex field ℂ. Key contributions:• A clean distinction between applying a pre-computed factorization on a crossbar (well supported) and solving a decomposition physically in a closed analog loop (supported only at small scale);• A complex-valued core: photonic phase as the imaginary axis, paired memristor cells for Re/Im conductance, and the spectral theorem for Hermitian operators—with an honest scope on where the Hermitian-real-spectrum property actually applies;• A radial/CORDIC datapath motivated by the geometry that already makes rotary position embeddings (RoPE) effective;• A context layer linking recurrent low-rank state (linear-attention/state-space) with rotation-based KV-cache quantization, observing that a photonic unitary mesh can physically supply the random rotation those methods perform in software;• An execution-integrity layer built from feasible primitives: measured boot, control-flow integrity, and an optical PUF;• A hardware-aware distillation pipeline that trains models directly into the substrate's low-rank, rotated, quantized, noisy regime—resolving the "apply ≠ solve" risk—and repurposes device noise as sampling temperature and physical decay as recurrent state, each with stated bounds. STATUS: This is a position and architecture paper, not a fabricated-and-measured result. ADAM-TPU has not been fabricated, taped out, or benchmarked. All claims are intended to be falsifiable; the open problems in §7 are part of the contribution. License: Released under the S.V.E. Meta-License v4.0 (share-alike).Repository: https://codeberg.org/skovnats/SVE-Systemic-Verification-EngineeringContact: [email protected] | ORCID: 0009-0002-1230-1639