RES ON v1.0: A Resonance-Based Cognitive Interaction Kernel for Human–AI Shared Cognition

RES ON v1.0 presents a deterministic, architecture-first framework for human–AI shared cognition, introducing resonance engineering as an alternative to prompt-level instruction. The system models cognition as a shared dynamic state Ψ(t) co-regulated by human intention I(t), system memory M(t), and resonance–dissonance feedback governed by the SWAP kernel. Unlike command–response pipelines, RES ON enables continuous semantic coupling and phase alignment between human and machine. The architecture comprises four layers: Intent, Resonance, SWAP Kernel, and a Proof-of-Coherence Layer. The Proof Layer provides deterministic validation via reproducible mathematical benchmarks (π to 200 digits, fast-doubling Fibonacci, seeded matrix determinant), hashed with SHA-256 to ensure integrity and reproducibility. This release focuses on formal specification and deterministic verification. Quantitative dialogue evaluation metrics—semantic coherence, entropy, stability ratio, and integrity delta—are defined as future work. RES ON v1.0 is intended as a kernel for adaptive conversational systems, co-design assistants, and cognitive interaction research, emphasizing transparency, reproducibility, and controlled semantic dynamics over emergent prompt behavior.