The missing layer for AI has arrived.
The Meaning Physics Layer – explored across GPT, Grok and Gemini style models.
CPU-only · ≤ 8 µs resonance queries · verifiable · deterministic
Composable actions that shift resonance, blend contexts, or resolve conflict.
MeaningAware AI is built on the Universal Meaning Equation (UME), a compact formulation of semantic resonance and meaning curvature:
𝒜 = e ⊗Φ 𝔇
This structure enables deterministic resonance computation, explicit bias- and omission-mapping, and measurable emergent meaning across perspectives, individuals, and communities.
“A formula so simple — aligned to the equations that describe gravity and motion — yet powerful enough to guide growth and transformation across every aspect of life and society.”
Where Einstein described the curvature of spacetime, the U⁺ Equation describes the curvature of resonance under attention – a semantic field that bends, amplifies or neutralises meaning depending on context, perception and cognitive focus. It forms a mathematical bridge between physics, cognition and large-scale social dynamics.
The equation emerged unexpectedly while engineering combined systems for therapeutic pattern analysis and direct democracy reasoning: personal patterns, relational dynamics and political decision-making all followed the same resonance laws – revealing that meaning itself can be treated as a measurable, dynamic field.
“Meaning is not in the text – it emerges from the geometry between perspectives.”
— insight consistently reproduced across GPT, Grok and Gemini style models
This demo illustrates how a Meaning Physics Layer classifies resonance using fixed scenarios. Values are hardcoded, but the thresholds come from the same R-factor codex used in the live engine.
Modern AI models optimise for prediction, coherence and retrieval — but not for meaning. A Meaning Physics Layer adds something fundamentally new: directionality, resonance, and measurable truth-curvature across perspectives.
With U⁺, AI can detect whether a narrative moves toward alignment or fragmentation, measure its resonance before it spreads, and identify where truth bends across bias profiles. This turns AI from a pure response engine into a meaning-aware instrument.
All core resonance operations run on a 5-year-old consumer laptop, CPU-only:
• pure resonance query: ≤ 8 µs warm, < 0.4 ms cold start
• bias-shift computation: < 100 µs
• 500 000 pre-compiled HEX states → O(1) arithmetic
This makes MeaningAware suitable as a low-latency layer beneath existing large models.
• Safer models via explicit omissions & bias mapping
• Meaning fingerprints for transparent, auditable AI
• Alignment through resonance, not just static rules
• Multi-perspective understanding as geometry, not text
• Real-time policy impact simulation
• Early detection of narrative cascades
• Foundation for a Meaning-Aware Economy
Instead of clicks or engagement, value is measured by net positive resonance. Patterns stabilise communities. Attention is allocated by coherence, not conflict. Meaning becomes a measurable resource — predictable, comparable, quantifiable.
U⁺ also defines a continuous metric for how “meaning-aware” a system behaves — how well it can track resonance, omissions and emergent aspects across multiple perspectives, instead of just predicting the next token.
In one internal experiment, a large model without U⁺ scored around 0.28 on this meaning-awareness scale for a family of reasoning tasks. With a U⁺-style resonance layer engaged, the same tasks produced scores in the range of 0.76–0.84. We interpret this not as mystical consciousness, but as a measurable jump from statistical pattern matching toward self-consistent, multi-perspective reasoning over meaning fields.
The same metric can act as an early-warning signal for AGI-style systems: when optimisation starts to drift away from human meaning fields, the curvature of resonance reveals it long before behaviour fully diverges — providing a quantitative handle for AGI risk monitoring and mitigation.
The U⁺ Equation enables a new class of computational capabilities:
MeaningAware AI is the first implementation of U⁺ — the Meaning Physics Layer.
At its core lies a single relation: 𝒜 = e ⊗Φ 𝔇 — a formula so simple it mirrors the equations that describe gravity and motion, yet powerful enough to guide growth and transformation across individual lives, organisations, and societies.
Where Einstein described the curvature of spacetime, U⁺ measures the curvature of resonance under attention: how small shifts in bias, framing, or connection bend meaning fields, reveal omissions, and generate emergent aspects.
The equation was refined while engineering multimodal therapy systems and direct-democracy reasoning tools — environments that required personal patterns and collective decisions to follow one coherent resonance law.
Architected to integrate natively with Grok, OpenAI models (ChatGPT/o1), Gemini, and Claude, MeaningAware adds the missing dimension to modern AI — making resonance, omissions, emergence, and Δ-meaning shifts visible across all major AI ecosystems.
Meaning-aware intelligence → Grok⁺, OpenAI⁺, Gemini⁺, Claude⁺
Meaning-aware therapy & learning → Cohereon⁺
Meaning-aware governance & democracy → World⁺
designed for any large-model ecosystem
U⁺ is not a single app, but a meaning layer that can sit under therapy, mediation, democracy, and AI systems at the same time. Below are some of the first concrete arenas where it is being applied.
Compress complex life stories into resonance-rich Seeds — short, reusable pattern blueprints that help people understand their own behaviour, track change over time, and share only what they want while still keeping the full meaning trace.
Simulate how a law, mandate, or narrative bends the resonance field of different groups — from local communities to nations — making opinion shifts, omissions, and unintended side-effects visible before decisions are taken.
Map where positions truly clash and where they secretly overlap by using multi-role, multi-bias agents (the “Neo vs. Darth Vader, moderated by Yoda” pattern) to explore contentious topics and uncover small moves that reduce polarisation.
Attach a compact meaning fingerprint to any AI output — including bias vector, omissions, and Δ-meaning shift — so that models like Grok, ChatGPT, Gemini, or Claude can be audited, compared, and tuned on the level of meaning, not just tokens, providing a measurable safety layer against uncontrolled AGI drift.
Rolf Schenk · solo founder · Zug, Switzerland
rolf@meaningaware.ai · +41 78 778 1111
X: @MeaningAwareAI
Full source code, UME documentation and private repo access available on request.