mirror of
https://github.com/hanzoai/papers.git
synced 2026-10-22 17:20:17 +00:00
zeekay
cb986b3335
Token IDs are a bijection with a published dictionary, so storing them is storing the prompt. Frozen-backbone embeddings are trained to preserve meaning, and that faithfulness is exactly what inverts — Morris et al. recover most short inputs verbatim from the vector alone. Both get called anonymization. Neither is. The stronger position is to not retain. Training wants a gradient; the request is the courier that carries it. Once the update lands, keeping the courier buys no learning and all of the risk. A retention window is a promise; an absent column is a fact. The argument's force is that it costs nothing — the low-dim features a router needs are cheaper to compute, cheaper to store, and on the evidence of the router already serving our traffic, sufficient. Privacy and efficiency here are one constraint seen twice, because both follow from minimality. Includes an audit of our own ledger, which fails this today: it persists a frozen-backbone embedding under a comment asserting 'never prompt text' — true, and doing a false job. A privacy argument never run against its author's own code is a marketing claim, so the check is the contribution.
Hanzo AI Research Papers
Technical papers covering Hanzo AI compute infrastructure, agent frameworks, ML inference, platform services, and defense applications. Includes the Zen model family co-developed with Zoo Labs Foundation.
About
Hanzo AI is an American-owned, Techstars-backed ('17) applied AI lab, founded 2014, building the Zen frontier model family alongside a vertically integrated, post-quantum AI and cryptography stack. The work spans frontier models, an agentic runtime, edge inference, and production post-quantum cryptography — engineered to run sovereign and disconnected in defense, regulated, and air-gapped environments rather than on centralized cloud. The platform these papers document is generally available at hanzo.ai.
2026 Highlights
Each claim below is developed in the catalogued papers (INDEX.md):
- Defense & Reindustrialization suite (16 papers + one-pager). Positioned for the
American industrial base — sovereign AI, robotics, post-quantum, and low-latency
on-chain coordination for factories, vehicles, drones, ships, and contested-edge
systems. Strategy & case studies: sovereign-vs-closed AI (
hanzo-vs-claude), the ~90% cloud-cost migration on real numbers (hanzo-cloud-economics), agentic engineering at regulated scale (hanzo-agentic-prowess), FHE-native confidential computing (hanzo-fhe-native), automated security auditing (hanzo-security), and audit-ready compliance (hanzo-compliance). Capability depth: autonomous agentic AI (hanzo-agentic-ai), DDIL/tactical assured networking (hanzo-assured-networking), FHE-mediated cross-domain MLS (hanzo-cross-domain), edge AI/ML (hanzo-edge-ai-ml), counter-exploitation (hanzo-countermeasures), full-spectrum cyber (hanzo-full-spectrum-cyber), quantum sensing/QKD (hanzo-quantum-sensing-qkd), verification & validation (hanzo-test-evaluation), and the ZAP protocol (hanzo-zap-protocol). Start with the one-pager (defense/hanzo-overview.pdf); the full suite is published at papers.hanzo.ai. - Engine / AI infrastructure (6 papers, measured on-device). A native Rust
train-and-serve engine benchmarked on real silicon: ROCm decode parity with llama.cpp
at 97% of the memory-bandwidth wall (
hanzo-rocm-inference), measured AMD/NVIDIA/Apple edge inference (hanzo-cross-backend-inference), native training inside the inference stack (hanzo-native-training), continuously-learning private AI (hanzo-continuous-learning-privacy), serving economics (hanzo-platform-infra-desktop), and the unifying thesis (hanzo-native-stack-thesis). Unusually honest — they name their own stubs and reverted kernels and claim no win over NVIDIA. - Unified sovereign-tenant cloud (measured). The whole multi-tenant cloud as one Go
binary — 56 in-process subsystems over shared-nothing storage, each tenant its own
post-quantum-encrypted SQLite file. Measured on the production open path: ≈0.25 MB RAM
per actively-served tenant, near-zero idle — memory scales with concurrency, not
registrations, so one billion registered tenants at 1% peak concurrency fits ≈20
commodity 128 GB nodes (
hanzo-unified-tenant-cloud; architecture companioncloud-unified-binary). - Production post-quantum cryptography. All three NIST PQC standards — FIPS 203
(ML-KEM), 204 (ML-DSA), 205 (SLH-DSA) — integrated into a live consensus system with
hybrid classical/post-quantum security (
hanzo-full-spectrum-cyber). - Formal verification. 50 machine-checked Lean 4 proofs, 18 cryptographic security
reductions (incl. ML-DSA EU-CMA, ML-KEM IND-CCA2), and NIST Known-Answer-Test
validation across FIPS 203/204/205 (
hanzo-formal-verification). - ZAP (Zero-copy Application Protocol). 2.2 ns parse latency, 11.5M transactions/s,
91% memory and 96% energy reduction vs JSON-RPC; native post-quantum transport
(ML-KEM-768 + ML-DSA-65) and W3C DID identity (
hanzo-zap-protocol). - Agentic runtime. 83 specialized agents and 15 orchestrators executing 200–300
sequential tool calls, fully offline on embedded ARM with quantized Zen models
(
hanzo-agentic-ai,hanzo-edge-ai-ml). - Zen frontier models. An 18-model family (0.8B–1T+ parameters), co-developed with Zoo Labs Foundation, with owned weights and full provenance.
Structure
Each paper lives in its own subdirectory:
papers/
├── shared/ # cover styles, lstlang.tex, paperkit
│ ├── hanzocover.sty
│ └── lstlang.tex
├── <paper-slug>/
│ ├── <paper-slug>.tex # main file (\input's sections)
│ ├── <paper-slug>.pdf # compiled output
│ └── sections/ # modular sections
│ ├── 01-intro.tex
│ ├── 02-architecture.tex
│ ├── 03-protocol.tex
│ ├── ...
│ └── 99-bibliography.tex
└── INDEX.md # auto-generated catalogue
Building
cd <paper-slug>
TEXINPUTS=".:..:" latexmk -pdf <paper-slug>.tex
Or build all:
make all
Index
See INDEX.md for full catalogue of papers.
Conventions
- One paper, one directory. No top-level .tex files.
- Modular sections. Main .tex \input's
sections/NN-name.texfiles for easy editing. - Shared cover. All papers use
\usepackage{shared/hanzocover}and\hanzocoverpage. - Shared lstlang. All papers use
\input{shared/lstlang}after\usepackage{listings}. - No AI slop. Technical, dense, citation-supported.
- One paper per concept. Updates over time via versioning, not duplication.