fix: luxcoverpage before maketitle — black cover on page 1 for all root papers

astle-reactive-sdk.tex

@@ -32,8 +32,8 @@ Hanzo Industries\\
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Astle.js, a reactive JavaScript SDK designed for building commerce applications with predictable state management and real-time synchronization. Astle.js introduces a unidirectional data flow architecture where application state is derived from an immutable event log, enabling time-travel debugging, optimistic updates, and seamless offline support. The SDK provides declarative bindings for React, Angular, and vanilla JavaScript, abstracting the complexity of real-time commerce operations—cart management, inventory tracking, and checkout flows—behind a consistent API. We evaluate Astle.js through integration with production e-commerce storefronts, demonstrating reduced development time, improved user experience through optimistic rendering, and simplified debugging of complex state transitions.

crowdstart-commerce.tex

@@ -32,8 +32,8 @@ Hanzo Industries\\
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Crowdstart, a cloud-native e-commerce platform designed from first principles for horizontal scalability and operational simplicity. Built on Google App Engine with Go as the primary implementation language, Crowdstart demonstrates that commerce infrastructure can achieve both developer ergonomics and production reliability without the operational burden of traditional deployments. Our architecture leverages eventual consistency, idempotent operations, and stateless request handling to enable automatic scaling from zero to millions of transactions. We describe the system design, evaluate performance characteristics, and discuss lessons learned from production deployments serving over 500 merchants.

earle-genetic-marketing.tex

@@ -32,8 +32,8 @@ Hanzo Industries\\
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Earle, a system for automated marketing optimization using genetic algorithms. Traditional A/B testing evaluates a small number of variants sequentially, limiting exploration of the combinatorial space of marketing parameters. Earle treats marketing campaigns as genomes—structured combinations of headlines, images, copy, and targeting criteria—and evolves populations of campaigns toward conversion objectives. Our approach enables simultaneous exploration of thousands of variants while automatically allocating budget toward high-performing individuals. We formalize the marketing optimization problem, describe our genetic algorithm implementation with domain-specific operators, and evaluate Earle on production campaigns across e-commerce, SaaS, and lead generation verticals. Results demonstrate 34\% average improvement in conversion rates compared to human-designed campaigns and 2.8$\times$ faster convergence than multi-armed bandit approaches.

hanzo-aci.tex

@@ -45,8 +45,8 @@
 \date{December 2024}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present \textbf{Hanzo ACI (AI Chain Infrastructure)}, a blockchain architecture purpose-built for decentralized AI compute markets. While general-purpose blockchains (Ethereum, Solana) can host compute marketplace smart contracts, their transaction throughput, finality latency, and verification mechanisms are ill-suited for the unique requirements of AI workloads: high-bandwidth data transfer, GPU-intensive computation, probabilistic verification, and multi-party job orchestration. ACI introduces three key innovations: (i) \emph{Proof of AI (PoAI)}, a novel consensus mechanism that combines TEE attestations with statistical verification games to verify AI computation results without re-executing the full workload, achieving 99.7\% detection rate of malicious providers at 0.3\% verification overhead; (ii) a \emph{multi-consensus architecture} that separates the fast-path (job matching, resource allocation) from the settlement-path (payment finalization, dispute resolution), achieving 200ms job matching latency with 2-second settlement finality; and (iii) an \emph{AI-native virtual machine (AIVM)} that supports tensor operations, model loading, and inference scheduling as first-class blockchain operations. We evaluate ACI on a testnet of 100 validator nodes, 500 worker nodes, and 1,000 simulated clients, demonstrating 12,000 job matches per second, 99.97\% settlement accuracy, and 73\% lower gas costs compared to equivalent Ethereum L2 implementations. We present formal security proofs for PoAI against collusion, free-riding, and Sybil attacks, and analyze the economic incentive structure that ensures honest participation.

hanzo-agent-grpo.tex

@@ -47,8 +47,8 @@ via GRPO Semantic Experience Management}
 \date{February 2026}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 % ============================================================================
 % ABSTRACT

hanzo-agent-sdk.tex

@@ -44,8 +44,8 @@
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Hanzo Agent SDK, a multi-agent orchestration framework that unifies agent execution, tool management, and memory systems under a single, extensible architecture. The framework introduces three key innovations: (1) a generic Agent dataclass with support for dynamic instructions, typed contexts, and composable guardrails; (2) a hierarchical memory system with semantic retrieval, automatic compression, and reflection capabilities; and (3) an AgentNetwork abstraction enabling parallel execution, shared state management, and intelligent routing between specialized agents. We demonstrate that multi-agent orchestration with semantic routing achieves 34\% higher task completion rates compared to single-agent approaches on complex, multi-step reasoning benchmarks. The SDK is designed for production deployment with comprehensive tracing, lifecycle hooks, and Model Context Protocol (MCP) integration.

hanzo-ai-commerce.tex

@@ -32,8 +32,8 @@ Hanzo Industries\\
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Hanzo, an AI-first commerce platform that integrates machine learning throughout the customer journey. Unlike traditional e-commerce systems that bolt AI capabilities onto existing architectures, Hanzo was designed from inception with artificial intelligence as the core organizing principle. The platform provides real-time personalized recommendations, intelligent search ranking, dynamic pricing optimization, automated fraud detection, and predictive inventory management through a unified AI infrastructure. We describe the system architecture, detail the machine learning models powering each capability, and evaluate performance across production deployments. Results demonstrate significant improvements: 28\% increase in average order value through personalization, 45\% reduction in fraud losses, and 18\% improvement in inventory turnover. Hanzo represents a new paradigm for commerce platforms where AI is not an add-on but the foundational layer.

hanzo-analytics-ml.tex

@@ -49,8 +49,8 @@
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Hanzo Analytics, a machine learning platform for e-commerce that provides predictive analytics, customer segmentation, and churn prediction. Our system processes behavioral event streams in real-time to construct customer profiles and predict future actions. We introduce a novel feature engineering pipeline that extracts 847 features from raw event data, enabling predictive models that achieve 0.89 AUC for purchase prediction and 0.84 AUC for churn prediction. Customer segmentation using our modified k-means algorithm with behavioral embeddings improves marketing campaign ROI by 2.3x compared to demographic-only segmentation. The platform processes 50M events daily across 500+ merchants.

hanzo-api-gateway.tex

@@ -49,8 +49,8 @@
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present the Hanzo API Gateway, a unified commerce API architecture supporting both REST and GraphQL interfaces. The gateway provides rate limiting, caching, authentication, and request routing for e-commerce operations. We formalize the API design principles, implement a token bucket rate limiter with distributed state, and introduce a novel caching strategy that achieves 94\% cache hit rates for product data. The system handles 180,000 requests per second at P99 latency of 23ms. Production deployments serve 1,200+ merchants with 99.99\% availability over 18 months.

hanzo-aso.tex

@@ -44,8 +44,8 @@
 \date{September 2025}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 % ============================================================================
 % ABSTRACT

hanzo-candle.tex

@@ -45,8 +45,8 @@
 \date{August 2024}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present \textbf{Hanzo Candle}, a Rust-based machine learning framework designed for high-performance inference of large language models and multimodal models. While Python-based frameworks (PyTorch, TensorFlow) dominate model training, their runtime overhead, garbage collection pauses, and GIL contention make them suboptimal for latency-sensitive inference serving. Candle provides a pure-Rust tensor library with three key innovations: (i) a \emph{zero-copy tensor engine} that eliminates memory allocation overhead through arena-based allocation and view-based reshaping, achieving 2.3x lower memory allocation rates than PyTorch during inference; (ii) a \emph{fused kernel compiler} that automatically fuses sequential operations into single GPU kernels, reducing kernel launch overhead by 47\% on transformer architectures; and (iii) a \emph{type-safe model definition API} that leverages Rust's type system to catch dimension mismatches, dtype errors, and device placement bugs at compile time rather than runtime. We evaluate Candle on inference benchmarks spanning language models (Llama 3, Mistral, Phi), vision models (CLIP, SAM), and multimodal models (LLaVA), demonstrating 1.4--2.8x throughput improvements over PyTorch and 1.2--1.6x over ONNX Runtime, with 3.1x lower P99 latency variance due to the absence of garbage collection. Candle supports CUDA, Metal, and CPU backends, enabling deployment from data center GPUs to Apple Silicon laptops. The framework has been adopted for production inference serving at Hanzo AI, processing 47 million inference requests over 14 months.

hanzo-chat.tex

@@ -44,8 +44,8 @@
 \date{March 2025}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present \textbf{Hanzo Chat}, a multi-model conversational AI system that enables users to interact with over 100 language models through a unified interface with persistent memory, tool integration, and adaptive model selection. Unlike single-model chat applications, Hanzo Chat introduces three key innovations: (i) a \emph{model routing layer} that dynamically selects optimal models based on query characteristics, cost constraints, and historical performance, achieving 23\% higher user satisfaction than fixed-model baselines; (ii) a \emph{hierarchical memory architecture} combining session, project, and global memory stores with retrieval-augmented context injection, supporting conversations spanning weeks without context window limitations; and (iii) a \emph{Model Context Protocol (MCP)} integration layer providing access to 260+ tools including code execution, web search, file manipulation, and API interaction. We evaluate Hanzo Chat on a suite of conversational benchmarks including MT-Bench, ChatBot Arena, and a novel multi-turn tool-use benchmark (MTTU-100), demonstrating that multi-model routing with memory achieves state-of-the-art performance while reducing inference cost by 41\% compared to always using the most capable model. We report results from 18 months of production deployment serving over 500,000 conversations, including analysis of model selection patterns, memory utilization, and tool usage statistics.

hanzo-checkout.tex

@@ -49,8 +49,8 @@
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Hanzo Checkout, a payment processing system designed to minimize transaction friction while maintaining PCI DSS Level 1 compliance. Our architecture introduces a novel one-click checkout paradigm that reduces cart abandonment by 47\% compared to traditional multi-step checkout flows. The system employs intelligent payment orchestration across multiple processors, automatic failover, and tokenized credential storage. We formalize the payment state machine, prove its safety properties, and demonstrate sub-200ms transaction initiation latency at scale. Production deployments across 2,000+ merchants processing \$1.2B annually validate our approach.

hanzo-dso.tex

@@ -49,8 +49,8 @@
 \date{December 2023}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 % ============================================================================
 % ABSTRACT

hanzo-federated-agents.tex

@@ -51,8 +51,8 @@ BitDelta-Compressed Semantic Optimization}
 \date{February 2026}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 % ============================================================================
 % ABSTRACT

hanzo-flow.tex

@@ -45,8 +45,8 @@
 \date{April 2025}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present \textbf{Hanzo Flow}, a visual workflow builder for constructing, debugging, and deploying multi-agent AI systems. While code-based agent frameworks provide flexibility, they impose high barriers to iteration, lack visibility into agent decision-making, and make debugging multi-agent interactions intractable. Hanzo Flow introduces three innovations: (i) a \emph{typed dataflow graph} formalism where nodes represent agent operations (LLM calls, tool invocations, control flow, human-in-the-loop gates) and edges represent typed data channels, enabling static verification of workflow correctness before execution; (ii) a \emph{visual debugger} with execution replay, token-level attribution, and branching exploration that reduces debugging time by 67\% compared to log-based debugging; and (iii) an \emph{adaptive execution engine} that supports parallel branch execution, dynamic subgraph expansion, and automatic retry with model fallback, achieving 94\% end-to-end success rate on complex multi-step tasks. We evaluate Hanzo Flow on the AgentBench suite, a novel visual workflow benchmark (FlowBench-200), and a user study with 48 developers. Results show that visual workflow construction reduces development time by 3.2x compared to code-based frameworks while achieving equivalent or superior task completion rates. Hanzo Flow has been used in production to build 1,200+ workflows processing 8.4 million agent invocations over 10 months.

hanzo-harness-hacking.tex

@@ -47,8 +47,8 @@
 \date{February 2026}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 % ============================================================================
 % ABSTRACT

hanzo-hmm.tex

@@ -36,8 +36,8 @@
 \date{September 2025}
 
 \begin{document}
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present the \textbf{Hamiltonian Market Maker (HMM)}, a physics-inspired automated market maker (AMM) that leverages the mathematical structure of Hamiltonian mechanics to price heterogeneous AI compute resources on decentralized exchanges. Traditional AMMs such as Uniswap's constant product formula operate effectively for fungible token pairs but fail when applied to multi-dimensional resource bundles with quality constraints, SLA requirements, and rapidly shifting supply-demand dynamics characteristic of AI compute markets. HMM formulates the market state as a point in a symplectic phase space $(\bm{q}, \bm{p}) \in T^*\mathcal{M}$, where generalized coordinates $\bm{q}$ represent resource reserves and conjugate momenta $\bm{p}$ encode demand pressures. The Hamiltonian function $\mathcal{H}(\bm{q}, \bm{p})$ serves as a conserved invariant that determines endogenous prices without external oracle dependency. We derive the full equations of motion governing price evolution, prove conservation laws analogous to energy and momentum in classical mechanics, and construct symplectic integrators that preserve the geometric structure of the phase space under discrete trading updates. We extend the framework to multi-asset pools with weighted resources, prove impermanent loss bounds strictly tighter than constant product and stableswap alternatives, and introduce a MEV resistance mechanism grounded in the non-commutativity of phase space flows. Extensive simulations over 180 days of synthetic trading data demonstrate that HMM achieves 15.3\% higher capital efficiency than orderbook baselines, 98.7\% price stability versus 89.2\% for oracle-based AMMs, quote latency under 200ms, and impermanent loss 32\% lower than Uniswap v3 concentrated liquidity positions. We provide formal security proofs against flash loan attacks, sandwich attacks, and oracle manipulation, and present results from a live testnet deployment with 50 worker nodes and 100 client agents.

hanzo-identity-nft.tex

@@ -44,8 +44,8 @@
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Hanzo Identity, a decentralized identity system where identities are bound to non-fungible tokens (NFTs) and secured through token staking. The system introduces: (1) a multi-network registry supporting identities across Hanzo, Lux, and Zoo blockchains with unified namespacing; (2) a length-based pricing model where shorter names require exponentially higher stakes, creating economic scarcity for premium identities; (3) a delegation mechanism enabling stake-weighted governance and reward distribution. The registry is implemented as a UUPS-upgradeable contract, processing over 50,000 identity claims in the first month with \$2.1M in total value locked. We analyze the token economics and demonstrate that the pricing model effectively prevents squatting while maintaining accessibility for standard-length identities.

hanzo-jin-architecture.tex

@@ -44,8 +44,8 @@
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Jin, a hypermodal AI architecture that unifies text, vision, audio, and extensible custom modalities through a joint embedding predictive framework. Jin combines three key innovations: (1) modality-specific encoders projecting heterogeneous inputs into a shared latent space with learnable modality embeddings; (2) a diffusion transformer backbone with Mixture of Experts (MoE) routing for efficient cross-modal reasoning; (3) hierarchical zooming JEPA (z-JEPA) for multi-scale visual understanding. Our experiments demonstrate that Jin achieves 82.1\% accuracy on ImageNet with zero-shot transfer, outperforms single-modality baselines by 15\% on AudioCaps retrieval, and enables novel cross-modal generation tasks. The architecture supports runtime modality extension without retraining the core model, enabling deployment flexibility for diverse applications.

hanzo-jin.tex

@@ -44,8 +44,8 @@
 
 \begin{document}
 
-\maketitle
 \luxcoverpage
+\maketitle
 
 \begin{abstract}
 We present Jin, a hypermodal AI architecture that unifies text, vision, audio, and extensible custom modalities through a joint embedding predictive framework. Jin combines three key innovations: (1) modality-specific encoders projecting heterogeneous inputs into a shared latent space with learnable modality embeddings; (2) a diffusion transformer backbone with Mixture of Experts (MoE) routing for efficient cross-modal reasoning; (3) hierarchical zooming JEPA (z-JEPA) for multi-scale visual understanding. Our experiments demonstrate that Jin achieves 82.1\% accuracy on ImageNet with zero-shot transfer, outperforms single-modality baselines by 15\% on AudioCaps retrieval, and enables novel cross-modal generation tasks. The architecture supports runtime modality extension without retraining the core model, enabling deployment flexibility for diverse applications.

hanzo-llm-gateway.tex

@@ -54,8 +54,8 @@
 \date{October 2025}
 
 \begin{document}
-\luxcoverpage
 
+\luxcoverpage
 \maketitle
 
 \begin{abstract}

hanzo-mcp-server.tex

@@ -43,8 +43,8 @@
 \date{May 2021}
 
 \begin{document}
-\luxcoverpage
 
+\luxcoverpage
 \maketitle
 
 \begin{abstract}

hanzo-ml-framework.tex

@@ -43,8 +43,8 @@
 \date{July 2022}
 
 \begin{document}
-\luxcoverpage
 
+\luxcoverpage
 \maketitle
 
 \begin{abstract}

hanzo-network-architecture.tex

@@ -22,8 +22,8 @@ Zach Kelling\thanks{Corresponding author: [email protected]} \\
 \date{Version: v2025.09 \\ September 2025}
 
 \begin{document}
-\luxcoverpage
 
+\luxcoverpage
 \maketitle
 
 \begin{abstract}

hanzo-operative.tex

@@ -54,8 +54,8 @@
 \date{April 2025}
 
 \begin{document}
-\luxcoverpage
 
+\luxcoverpage
 \maketitle
 
 \begin{abstract}

hanzo-sdk-ecosystem.tex

@@ -48,8 +48,8 @@
 \date{July 2019}
 
 \begin{document}
-\luxcoverpage
 
+\luxcoverpage
 \maketitle
 
 \begin{abstract}

hanzo-webhooks.tex

@@ -48,8 +48,8 @@
 \date{November 2019}
 
 \begin{document}
-\luxcoverpage
 
+\luxcoverpage
 \maketitle
 
 \begin{abstract}

verus-analytics.tex

@@ -31,8 +31,8 @@ Hanzo Industries\\
 \date{November 2015}
 
 \begin{document}
-\luxcoverpage
 
+\luxcoverpage
 \maketitle
 
 \begin{abstract}