Hykon v4.1 — A Conversational Governance Overlay for Large Language Models

Hykon v4.1 presents a conversational governance overlay for large language models, focused on managing epistemic risk during interaction rather than improving model accuracy or training outcomes. The framework introduces a stability-based control layer that evaluates conversational conditions in real time and enforces graduated responses—including scoped answers, explicit uncertainty, or clean halts—when epistemic risk exceeds safe bounds. Stability scores are treated as diagnostic signals, not correctness estimates or optimization targets, and a perfect score is explicitly prohibited. This release formalizes Hykon v4.1 in an ML-facing research format. It does not modify underlying model weights, does not introduce reward functions, and does not propose training or fine-tuning procedures. Instead, it demonstrates how conversational behavior can be governed through structural constraints, reflexive auditing, and halt conditions applied at inference time. The paper situates Hykon alongside related work in calibration, selective prediction, and hallucination mitigation, while remaining intentionally orthogonal to optimization-based alignment approaches. Appendices describing scoring rubrics, prompt templates, and experimental logs are marked as planned extensions and will be released as separate artifacts. Hykon v4.1 is intended as a bounded, transparent governance mechanism for evaluating when a system should respond, how far it should go, and when it should stop, rather than a method for improving answer quality or confidence.