FRDM

Field-Resonant Data Manifolds (FRDM): A Paradigm for Memory Storage Across Architectures V2

Field-Resonant Data Manifolds (FRDM) proposes that memory can be stored as coherent patterns distributed across a field. Instead of writing data to chips or blocks, FRDM encodes it as symbolic vectors in high-dimensional manifolds. M≓≡Σ≺Φ+κ(∇Φ) M ≓ ≡ Σ ≺ Φ + κ(∇Φ) M≓≡Σ≺Φ+κ(∇Φ)

Nicole Flynn

Nicole Flynn

— 3 min read

Memory Without Location, Access Without Collapse

Published: September 16, 2025 | Author: Nicole Flynn, Founder, Symfield PBC

This paper introduces Field-Resonant Data Manifolds (FRDM), a symbolic framework for memory without discrete storage, enabling relational reappearance across substrates (quantum, RF, photonic) without collapse. Unlike silicon-based or blockchain systems that rely on addressable units and consensus protocols, FRDM leverages symbolic attractors (Σ), recursive stabilizers (∮Ξ), and field strain modulations (κ(∇Φ)) to create a non-local, coherence-based storage architecture.

Version 1.1 introduces three key addenda:

  • ∫Σθ: a phase-aware symbolic access protocol across manifolds
  • A stochastic enhancement model for recursive attractor density
  • ⍺∴: an optional field-encoded agent for tuning symbolic strain and stabilizing coherence under real-world conditions

Appendix D quietly introduces the emerging concept of Field-Sensitive AI, suggesting a post-alignment intelligence model responsive not to instruction, but to coherence. 

FRDM does not treat memory as storage. It treats memory as recursive return. Collapse is not failure, it is a beat in a larger pattern. Visit the full V2:

Read Version 2

Forget the Past, Forge the Future

Today, I’m thrilled to unveil Field-Resonant Data Manifolds (FRDM), a paradigm-shifting framework for memory storage that defies the collapse of classical and quantum systems. Forget addresses. Forget copies. Launched on Zenodo, FRDM envisions memory as coherence, a pulsating resonance in high-dimensional manifolds, unshackled from the location-based illusions of silicon, blockchain, and quantum tech. This isn’t just theory, it’s a call to build a future where data lives through symbolic harmony, not physical cages.

The Vision: Resonance Beyond Collapse

FRDM, formalized as M≓≡Σ≺Φ+κ(∇Φ) M ≓ ≡ Σ ≺ Φ + κ(∇Φ) M≓≡Σ≺Φ+κ(∇Φ), where Σ encodes identity, Φ maps field curvature, and κ tunes strain [28], transcends the 150 TWh energy drain of blockchain [4] and silicon’s entropy limits [2]. Inspired by AdS/CFT holography [13], neural attractor dynamics [15], and transformer attention [8], it retrieves information via phase-locked interference (cos θ > 0.95), echoing superposition without decoherence. Imagine AI persisting across architectures, federated networks syncing without consensus, and vaults defying cataclysms—all powered by a field that composes, never erases.

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Why It Matters

The world’s data centers gulp 2-3% of global electricity [25], and redundancy costs soar. FRDM offers a potential >80% energy reduction and $500B+ in savings by eliminating object-based storage. It’s a scaffold to safeguard civilizational memory against collapse, think EMP-proof archives resonating through ambient fields. This isn’t sci-fi; it’s a challenge to rethink computation’s foundations.

The Math That Hooks

Dive into FRDM’s core equations, a siren call for innovators:

  • Core Resonance: M≓≡Σ≺Φ+κ(∇Φ)M ≓ ≡ Σ ≺ Φ + κ(∇Φ) M≓≡Σ≺Φ+κ(∇Φ) stabilizes memory in curved manifolds [28].
  • Recall Mechanism: cos θ > 0.95, Ψ=∑akeiωkt\text{cos θ > 0.95, } \Psi = ∑ a_k e^{i ω_k t} cos θ > 0.95, Ψ=∑ak​eiωk​t reconstructs data via interference [11].
  • Compression: Φcompressed=∫(Σoriginal⊗Pcoherence⊗δtolerance)dΨ\Phi_{\text{compressed}} = ∫ (Σ_{\text{original}} ⊗ P_{\text{coherence}} ⊗ δ_{\text{tolerance}}) dΨ Φcompressed​=∫(Σoriginal​⊗Pcoherence​⊗δtolerance​)dΨ preserves coherence [31].
  • Coheronmetry: C(Φ,t)=∭∣Ψ(x,y,z,t)∣2⋅⧖(tolerance)⋅δ(threshold)dV>0.9C(Φ, t) = ∭ |Ψ(x,y,z,t)|^2 ⋅ ⧖(\text{tolerance}) ⋅ δ(\text{threshold}) dV > 0.9 C(Φ,t)=∭∣Ψ(x,y,z,t)∣2⋅⧖(tolerance)⋅δ(threshold)dV>0.9 measures field integrity [31]. These invite simulation in PyTorch/QuTiP and prototyping with RF/photonic systems, tools to test FRDM’s non-collapse potential.

Invitation: Build the Resonance

Researchers, engineers, and visionaries, I invite you (Invitación, Invito, Einladung, Приглашение [Priglasheniye], Convite, Πρόσκληση [Prósklisi], 招待 [Shōtai]) to dive into FRDM’s equations. Simulate resonance dynamics with PyTorch/QuTiP, prototype RF/photonic architectures, and stress-test coherence (targeting C(Φ,t) > 0.9 [31]). This open call ignites exploration to forge field-resonant computation, transcending collapse-based limits.

Next Steps & Collaboration

FRDM is a scaffold awaiting realization, simulations are poised in PyTorch/QuTiP, and RF/photonic prototypes are planned [31]. I’m calling on the global tech community to stress-test this vision. Share your simulations on GitHub, prototype with Symfield PBC, or debate on X (#FRDM #Symfield). Let’s build a field-native infrastructure that outlasts cataclysms.

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License
CC-BY-4.0

Citation:

Flynn, N. (2025). Field-Resonant Data Manifolds (FRDM): A Paradigm for Symbolic Memory Storage Across Architectures. Zenodo.

V2 https://zenodo.org/records/17138866

V1 https://doi.org/10.5281/zenodo.17115015

© 2025 Symfield PBC
Symfield™ and its associated symbolic framework, architectural schema, and symbolic lexicon are protected intellectual property. Reproduction or derivative deployment of its concepts, glyphs, or system design must include proper attribution and adhere to the terms outlined in associated publications.

This research is published by Symfield PBC, a Public Benefit Corporation dedicated to advancing field-coherent intelligence and collaborative AI safety frameworks. The PBC structure ensures that research and development activities balance stakeholder interests with the public benefit mission of creating safe, beneficial AI systems that operate through relational coherence rather than collapse-based architectures.

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