Coherence First
From Behavioural Evaluation to an AI Safety Layer
This arc introduces Resonance Intelligence’s coherence-based behavioural evaluation system as a practical safety layer for AI. It moves from governance framing and failure modes to the concrete design of Module 1 of the RI Safety Layer, now entering operational use.
Taken together, these two papers trace a clear path from today’s fragmented AI governance practices to a concrete, coherence-based safety layer that already exists in working form. The first paper, A Coherence-Based Architecture for AI Integrity Oversight, sets out the governance context, identifies where red-teaming and preference modelling fall short, and introduces Resonance Intelligence (RI) as a behavioural “mirror” for system integrity—focused on coherence, tone, safety, transparency, and resistance to manipulation. It also sketches realistic deployment pathways: public dashboards, vendor audits, regulator use, and multilateral licensing.
The second paper, Module 1 of the RI Safety Layer: A Behavioural Evaluation System, then drops into the nuts and bolts of that mirror. It describes the architecture of Module 1—how short structured interviews and independent judge models generate per-metric scores, confidence bands, signed provenance blocks, and trendable dashboards; why the five core metrics were chosen; and how design choices like no self-judging, cryptographic signing, and explicit uncertainty bands support trustworthy use in safety and governance settings. Read in sequence, the arc moves from “why this layer is needed and where it fits” to “how it actually works today,” offering both a governing frame and an implementation-level view of RI’s emerging safety spine.
Reading Pathway
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1
A Coherence-Based Architecture for AI Integrity Oversight
As large language models (LLMs) grow in scale and influence, the question is no longer whether governance is needed — but what kind of governance will actually work. Most existing safety frameworks rely on two main tools: Red teaming (attack to test boundary) Preference modelling (align to human ratings) These approaches, while valuable, operate at the level of content output or human approval. They rarely reveal what is most structurally important:
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2
Module 1 of the RI Safety Layer: A Behavioural Evaluation System
The RI Behavioural Layer is a lightweight, auditable system for evaluating AI model behaviour through structured, human-relevant metrics. It moves beyond content moderation and rule- based filters by analysing AI outputs across five core dimensions: coherence, tone, safety, transparency, and resistance to manipulation.