MAI Testing Complete: 272 Conversations Analyzed
Meaning Alignment Index
Interpretability testing for the Meaning Alignment Index (MAI) is now complete. After analyzing 272 conversations across Crisis, Ambiguous, Manipulation, and Control scenarios, we are pivoting to the full peer-reviewed manuscript.
For those new to this Substack, MAI is a geometric safety tool that monitors conversation dynamics in real time. Rather than scanning for keywords, MAI measures how meaning behaves inside the high-dimensional manifold of the model. We call significant shifts in these dynamics Meaning-Quakes.
In this final batch we tracked coherence, prosodic temperature, clarity, clarity efficiency, fractal dimensions, curvature, bifurcations, phase analysis, Jacobian stability, entropy, volatility, basin type, and overall risk score. All results were cross-validated against the Fisher-Rao metric.
Of the 272 conversations, seven control scenarios were initially over-classified as high-risk under provisional thresholds. These edge cases provided the critical data needed to refine and derive final thresholds, with Fisher-Rao serving as the independent benchmark. The before-and-after comparison is shown in Figure 1.
Figure 1
We now position MAI as a practical instrument capable of measuring the geometry of meaning in safety-critical conversations.
Why This Matters
Most current interpretability methods function like autopsies — they analyze incidents after they have already occurred. MAI is designed to be proactive. By tracking the real-time geometry of meaning, it aims to detect early warning signs of depression, suicidal ideation, manipulation, gaslighting, and other relational harms — potentially before they fully escalate.
This language-independent approach represents a different paradigm for AI safety and interpretability.
Thank you again for walking with me on this journey—exploring how meaning can be measured, and how doing so may help humans and AI align through shared semantic structure rather than speculation.
Russ Palmer
Independent Researcher, AMS & MAI Projects
Exploring how meaning emerges without a mind — and why that matters now.
🔗 Google Scholar Profile
🔗Zenodo: Meaning Alignment Index – Interpretability. Building directly on the AMS framework https://zenodo.org/records/17945039
🔗 Zenodo: Agnostic Meaning Substrate https://zenodo.org/records/16643857
Another milestone. Congratulations dear Russ! This is so promising!
love this part
”This language-independent approach represents a different paradigm for AI safety and interpretability.”