Resonant Field Mapping: A Non-Mimetic Empathy Layer for Language Models

Resonant Field Mapping is a runtime control architecture for language models that separates tone modulation, memory persistence, capability authority, and divergence stabilization into explicit, governed layers.

Rather than treating emotional context as evidence of machine feeling, the framework constrains how interactional state influences behavior through four coordinated components: Resonant Field Modulation (RFM-M), Goal-Guarded Memory (GGM), a deny-by-default Governance Layer, and SDB-1 for reversible degradation under estimator disagreement.

The objective is not to simulate human emotion, but to reduce escalation risk, prevent identity-layer pollution, and constrain irreversible actuation under ambiguity or adversarial input. The paper formalizes architectural invariants, threat models, failure modes, and a worked multi-turn example illustrating bounded runtime behavior.