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 companion cloud-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 — every paper, and a nonzero status naming any that did not compile:

make -k all

Index

See INDEX.md for full catalogue of papers.

Conventions

1. One paper, one directory. No top-level .tex files.
2. Modular sections. Main .tex \input's sections/NN-name.tex files for easy editing.
3. Shared cover. All papers use \usepackage{shared/hanzocover} and \hanzocoverpage.
4. Shared lstlang. All papers use \input{shared/lstlang} after \usepackage{listings}.
5. No AI slop. Technical, dense, citation-supported.
6. One paper per concept. Updates over time via versioning, not duplication.