Aleph — a structure-complete memory fabric

Read the whole book from any address. One logical memory, forward-compatible across classical, photonic, and quantum hardware.

Today a language model generates causally — left to right — re-attending over an ever-growing transcript and re-deriving what it already knows. Aleph is a memory model in which the entire structure of an artifact is recoverable from any single point (the defining property of a hologram), so a generator can read the rest of the book instead of recomputing it. In the reference benchmark that turns an O(N²) access pattern into O(N) — a speedup that grows with size: 17× at 10 sections, 77× at 40, 145× at 100.

The same logical model runs on a hash table today and on a rare-earth quantum-memory crystal tomorrow, because the substrate is hidden behind a five-operation interface.

from aleph import AlephMemory, Codex, Slot
from aleph.backends import ClassicalBackend, PhotonicHolographicBackend, AFCBackend

mem = AlephMemory(ClassicalBackend(), codex)        # runs today
# mem = AlephMemory(PhotonicHolographicBackend())   # optical, when available
# mem = AlephMemory(AFCBackend(finesse=12))         # quantum, when available — same code

view = mem.read_whole_book("section_7")   # outline + constraints + recalled context, not the raw transcript

The one idea

A hologram: every fragment encodes the whole. Spectral–spatial holography in rare-earth crystals is literally this, in frequency–space. Aleph lifts it to the logical layer: store information so the global structure is recoverable from any address. That is exactly the access pattern an LLM needs to stop re-deriving context.

Why it can span quantum + photonic + classical

Because one material family does all three. Rare-earth-ion-doped crystals (Eu³⁺:Y₂SiO₅, Pr³⁺:Y₂SiO₅, Er³⁺ hosts) store information:

Regime	Mechanism	Demonstrated
classical	spectral hole burning (frequency-multiplexed bits)	decades of optical data storage
photonic	spectral–spatial holography / photon echoes	many patterns multiplexed in one volume
quantum	atomic frequency comb (AFC), EIT, CRIB/GEM	1-hour optical storage; ~370-min coherence; 30+ h spin lifetime (2025)

So "forward-compatible across substrates" isn't a bet on three technologies converging — it's three regimes of the same crystal. Room-temperature engineering uses phase-change (Ge–Sb–Te) and integrated photonics now; REIC is the quantum horizon. Full detail: docs/01-scientific-foundations.md.

The efficiency, measured

python -m aleph.bench (numbers produced by the run, not hard-coded):

slots	causal work	aleph work	speedup	recompute avoided	retries avoided
10	4,195	244	17.2×	30	5
40	92,560	1,204	76.9×	120	35
100	610,030	4,204	145.1×	300	95

Three savings, all real: bounded global view instead of a growing transcript (O(N²)→O(N)), derive-once content-addressed shared facts, and zero constraint backtracking because the whole plan is known up front. The benchmark's LLM is a faithful cost model, not a transformer — see docs/04 for exactly what is and isn't claimed.

A new model, additive to the industrial stack

The Aleph Inference Fabric (docs/07) doesn't replace the proven techniques — it composes them on one content-addressed substrate. Each is a tier: PagedAttention/vLLM (KV pages), prompt caching (prefix), RAG over HNSW/FAISS (long-term), MoE-style routing, Merkle/IPFS dedup, LSM tiering — plus Aleph's novel "whole-book" working set. Run it: python -m aleph.fabric → on a realistic workload it measures 97.5% prefix-hit and 2.47× KV-page dedup, every request served from a tier (real cache metrics, computed from the run).

Real-model validation — honest

A single-call test on a live transformer (OmniCoder-9B via Ollama) did not reproduce the cost-model speedups — it measured ~1.0× (twice). The fixed model template dominates token count at small N, and prompt-length on independent calls can't see cross-request KV reuse — which is where the savings actually live. The full write-up, diagnosis, and what would confirm it is in VALIDATION.md. The aleph.bench figures are correctly labelled as cost-model properties of the access pattern, not measured transformer wall-clock.

Architecture

        application / any language  ──────────────┐
                                                   │  spec/aleph.idl  (5 ops + 1 address rule)
        ┌──────────────────────────────────────────┘
        │   AlephMemory  ·  Codex ("the book")  ·  read_whole_book()
        │
        │   Backend interface:  write · read · query · reconstruct · stats
        ├── ClassicalBackend            RAM / phase-change      (runs — reference)
        ├── PhotonicHolographicBackend  spectral-spatial optics (simulation)
        └── AFCBackend                  rare-earth quantum AFC  (simulation)

Content addressing (BLAKE2b-128 of canonical payload) is the bridge: equal meaning → equal address, on every substrate, in every language.

Quickstart

git clone https://github.com/cognis-digital/aleph-memory && cd aleph-memory
python -m aleph.bench                              # the efficiency benchmark
python examples/demo_structured_generation.py      # same loop, 3 substrates
pip install -e ".[dev]" && pytest -q               # 10 tests

Pure standard library — no dependencies — so it runs anywhere.

Repo map

• aleph/ — runnable reference (model, codex, llm cost-model, 3 backends, benchmark)
• docs/ — the whitepaper: foundations · logical model · forward compatibility · read the whole book · spec & bindings · roadmap & open problems · inference fabric · glossary
• VALIDATION.md — honest real-model validation (and why it measured ~1.0×)
• spec/ — language-neutral interface + container format
• bindings/ — C ABI + Rust/Go/TypeScript surfaces (one interface, every language)

What's real vs. forward-looking

The classical backend, the Codex access model, the benchmark, and the tests run today. The photonic and quantum backends are honestly-labelled physics-inspired simulations that prove the interface is substrate-neutral and let you build against future hardware now. No speedup in this repo is asserted without python -m aleph.bench reproducing it. The hard open problems are in docs/06.

References (physics)

• One-hour coherent optical storage in an AFC memory (Eu³⁺:Y₂SiO₅) — Nature Communications (2021).
• Long optical coherence via dynamical decoupling in Eu³⁺:Y₂SiO₅ (~6 h class).
• Long spin lifetimes in Eu³⁺:Y₂O₃ ceramics for quantum memories — Communications Physics (2025).
• Time-bin qubit storage in rare-earth crystals — npj Quantum Information (2022).

This README synthesizes published results; it does not reproduce them.

License

Apache-2.0 © Cognis Digital.