Coherism and ALFM: A Unified Framework of Feedback Loops from Quantum Gravity to Enterprise AI

David Ahmann's Coherism project explores two parallel research directions—Coherism (quantum gravity with coherent state backreaction) and ALFM (adaptive learning for enterprise AI)—unified by the core concept of feedback loops for structure emergence via error correction. This work reveals deep information-theoretic connections between fundamental physics and artificial intelligence, with both fields leveraging error correction to drive dynamic structure formation (spacetime geometry in physics, model behavior in AI).

Quantum gravity (studying spacetime and black holes) and enterprise AI (deploying frozen LLMs) seem unrelated, but the Coherism project bridges them. Coherism focuses on how quantum coherent states affect spacetime geometry, while ALFM addresses the challenge of making frozen LLMs learn from errors without retraining. Both share the underlying structure of feedback loops for error correction leading to structure emergence.

Coherism proposes a semi-classical gravity framework with an information stress tensor Θ_μν, where relative entropy S(ρ‖σ_β) (between quantum state ρ and reference state σ_β) acts as a gravity source. Key predictions:

• BEC density modulation difference (~1e-6) in acoustic black hole near-horizon regions.
• Differential observable ΔA ≈2.3e-7 (direct entropy injection measurement).
• Falsification: if ΔA <5e-8, the acoustic version is invalid. Technical validation includes scripts like bec_sonic_horizon_simulation.py (acoustic models) and gpe_protocol_simulation.py (1D GPE kernel with 0.5% precision).

ALFM solves frozen LLM issues (no instant learning, risk calibration, tenant isolation) via a wrapper architecture: User input → [Frozen backbone + NEP memory + Consensus engine] → Decision → Output/Reject Core components:

• NEP: Negative Evidence Prior (vectorized failure memory for self-doubt calibration).
• Consensus Engine: Multi-agent arbitration balancing intuition and rules.
• Three-Tier Adapters: Ensure safe learning and tenant isolation. ALFM-BEM extends it with bidirectional experience memory (stores success/failure, active learning for OOD inputs, bounded adapters for stability).

Coherism: Simulations validate predictions and quantify system errors; compatible with the Weak Equivalence Principle (WEP violation η_coh ~1e-30·α, below detection limits). ALFM: Medical scenario results:

• Failure retrieval F1: ~0.59; success retrieval rate: ~0.70 (bidirectional, unlike RAG).
• OOD detection AUC: ~1.0 (clustering mode).
• Rejection rate: from ~11.6% → ~2.5% (final window ~1.2%).
• Query gain: ~6.2% accuracy boost in high uncertainty. Key RAG difference: BEM stores experience with results (auto-learns from deployment, no manual curation) vs RAG's document storage.

Coherism and ALFM share a core paradigm—structure emerges via continuous error correction:

Dimension	Coherism (Physics)	ALFM (AI)
Basic Object	Quantum state ρ & reference σ	Input query & experience memory
Core Metric	Relative entropy S(ρ‖σ)	Semantic similarity + risk signal
Feedback Mechanism	Info stress tensor backreaction	NEP update + adapter adjustment
Emergent Structure	Spacetime geometry correction	Model behavior improvement
Key Insight	Error correction → geometry	Error correction → intelligence
Both show structure is dynamic, not static—formed via ongoing error correction.

The project's value:

1. Cross-disciplinary unification: Basic physics and AI share conceptual foundations (feedback loops).
2. Feedback universality: Error correction may be a universal principle for complex system emergence.
3. Falsifiability: Both physics predictions and AI systems are verifiable/falsifiable.
4. Open science: Full open code (Zenodo DOIs) ensures reproducibility. Implications:

• Physics: Coherism offers a quantum-gravity interface testable via acoustic black hole experiments.
• AI: ALFM solves frozen LLM deployment pain points.
• Cross-disciplinary: Encourages bridging unrelated fields to find deep unities.