Resonant Field Storage (RFS)
The SMARTHAUS field-native memory substrate. Associative resonance paired with AEAD-backed exact recall, governed by quantitative guardrails.
Overview
Memory as a Field
RFS encodes every shard as amplitude + phase within a shared 4‑D lattice Ψ(x,y,z,t). No external index—structure and storage are the same artifact.
Associative + Exact Recall
Matched filters surface resonant peaks in milliseconds. When precision matters, AEAD-backed byte recall reconstructs the canonical payload.
Governed by Math
Field efficiency (η), quality (Q), and capacity headroom are measured continuously. Fail-close policies keep the lattice within proven bounds.
How RFS works
Encode
Deterministic encoders project content into complex vectors. Sparse operators Hₖ spread energy, while unitary FFT/IFFT cycles keep amplitudes bounded.
Assemble
All shards accumulate in the same lattice. Capacity guardrails throttle inserts, and every write is mirrored to a WAL + snapshot for replay.
Resonate
Queries excite resonant modes across the lattice. Peaks ranked by signal-to-noise expose context, provenance, and suggested follow-ups.
Recall
When exact content is required, the byte channel inverts the encoding pipeline and validates integrity with AES-GCM tags.
Why it works
Lemma-backed Stability
Unitary transforms prevent energy blow-up (Lemma 1). Controlled interference bounds ensure constructive peaks stay meaningful (Lemma 2).
Predictable Recall Error
Recall error decomposes into basis + projector contributions (Lemma 3). Calibration runs tune each term before onboarding new content.
Capacity & Governance
Efficiency η and thermal budgets trigger auto-drain before saturation (Lemma 4). Privacy, retention, and access policies are enforced per shard.
Performance & benchmarks
Resonance latency
< 35 ms
Median top-k resonance on 8×A100 nodes (1M shards).
Byte recall integrity
100%
AES-GCM validation on exact recall payloads in CI + prod.
Sustained ingest
> 12k shards/min
Validated across bursty workloads with η ≥ 0.35 headroom.
Footprint
~4.6 GB
Demo-sized lattice (5 GB cap) incl. WAL + snapshots (daily).
Why teams deploy RFS
Platform teams replace brittle vector stacks with deterministic memory. ML researchers gain provenance for every result. Compliance and trust offices prove retention, integrity, and deletion without stitching logs.
Explore the detailed scenarios and outcomes inside our use-case library.
Use-case portfolio
- Mission-critical knowledge retrieval
- Real-time incident response copilots
- Enterprise product intelligence
- Model alignment memory for multi-LLM orchestration
Ready to evaluate RFS?
Dive into the architecture, review the proofs, benchmark the sandbox, then schedule a technical deep dive with the team that built the field-native memory substrate.