mirror of
https://github.com/lux-apps/papers.git
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zeekay
dd93cb41f7
Sweep across the non-Tier-1 paper catalogue (Tier-1 owned by parallel
agent and excluded). All 113 processed papers already had luxcover
wired up; this commit fixes 14 build breakers and produces fresh
PDFs across the catalogue.
Fixes:
- lux-blockchain-scaling-laws: em-dash inside lstlisting -> --
- lux-corona-pq: stale \input pointing at 03-the-corona-scheme renamed
to 03-the-corona-scheme (file already renamed on disk)
- lux-lattice-cryptography-primer: same Corona->Corona rename
- lux-formal-verification-lean4: Lean source uses unicode (×, ∀, ∃, ∈,
∧, ∨, ∩, ≠, ⟨, ⟩, ·, ¬) inside lstlistings. Added a literate map
so listings render the math glyphs without UTF-8 fault.
- lux-id-did-specification: ✓/✗ in tabular replaced with \ding{51}
/ \ding{55} (pifont). Added pifont package.
- lux-ntt-simd-implementation: middle-dot in Go assembly listings;
added literate {·}{\textperiodcentered}.
- lux-ntt-transform: μ, ×, –, — in regular text (no T1 fontenc).
Replaced with $\mu$/$\times$/--/--- via unicode->latex sweep.
- lux-perpetuals-derivatives, lux-quantum-consensus,
lux-quasar-consensus, lux-tee-computing-mesh: same unicode->latex
sweep across body text. Also fixed broken math segment
`$L_{avg} = 10$\times$$` -> `$L_{avg} = 10\times$`.
- lux-zchain: emoji glyphs in roadmap table replaced with
parenthetical text.
- lux-triple-proof-consensus: dropped duplicate \usepackage{geometry}
that clashed with luxcover (geometry option clash).
- pq: defined missing \Rq macro.
PDFs rebuilt for the full Tier-2/3 set.
Excluded (Tier-1, parallel agent): lp-073-pulsar, lp-stricte2epq,
dag-evm-formal, lp-010-quasar-stm-{3,4}, lp-020-quasar-consensus,
lux-institutional-custody, lux-nist-mptc-submission, pqsafe-il5,
pq-rns, plus shared/ and lps/.
79 lines
3.2 KiB
TeX
79 lines
3.2 KiB
TeX
\documentclass[11pt,letterpaper]{article}
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\usepackage{../shared/luxcover}
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% Packages
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{amsmath,amssymb,amsthm}
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\usepackage{graphicx}
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\usepackage{hyperref}
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\usepackage{algorithm}
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\usepackage{algorithmic}
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\usepackage{booktabs}
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\usepackage{listings}
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\input{../shared/lstlang}
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\usepackage{xcolor}
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\usepackage{cite}
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% Solidity code highlighting
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\lstdefinelanguage{Solidity}{
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keywords={pragma, solidity, contract, function, returns, external, payable, view, require, if, else, for, while, return, address, uint256, bytes32, bytes, bool, mapping, event, indexed, calldata, memory},
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keywordstyle=\color{blue}\bfseries,
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ndkeywords={class, export, boolean, throw, implements, import, this},
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ndkeywordstyle=\color{darkgray}\bfseries,
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identifierstyle=\color{black},
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sensitive=false,
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comment=[l]{//},
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morecomment=[s]{/*}{*/},
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commentstyle=\color{purple}\ttfamily,
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stringstyle=\color{red}\ttfamily,
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morestring=[b]',
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morestring=[b]''
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}
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\lstset{
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language=Solidity,
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basicstyle=\small\ttfamily,
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breaklines=true,
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frame=single,
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showstringspaces=false
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}
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\title{\textbf{Lux TEE Mesh: Decentralized Confidential Computing Infrastructure for Blockchain Networks}\\
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\large Version v2025.07}
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\author{
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Zach Kelling\thanks{Corresponding author: [email protected]}\\
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\textit{Lux Industries, Inc.}\\
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\texttt{[email protected]}
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}
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\date{2024}
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\begin{document}
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\luxcoverpage
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\begin{abstract}
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We present Lux TEE Mesh, a decentralized confidential computing infrastructure that extends blockchain networks with hardware-based Trusted Execution Environments (TEEs). The system enables privacy-preserving off-chain computation while maintaining cryptographic verification on-chain. Unlike existing solutions that rely on a single TEE vendor, Lux provides a unified attestation framework supporting Intel SGX/TDX, AMD SEV-SNP, and NVIDIA Hopper GPU TEEs. The architecture achieves linear scalability by separating transaction ordering (on L1) from computation (in TEEs), while ensuring data confidentiality through hardware memory encryption. Workers stake collateral and provide cryptographic attestation receipts that smart contracts verify before accepting results. We demonstrate that this design enables confidential DeFi strategies, private LLM inference, and secure analytics with $O(n)$ throughput scaling, where $n$ is the number of worker nodes. Our economic model uses dynamic pricing and fraud-proof mechanisms to ensure honest behavior. Benchmark results show the system handles 10,000+ confidential jobs per hour with sub-second attestation verification latency.
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\end{abstract}
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\input{sections/01-introduction}
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\input{sections/02-system-architecture}
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\input{sections/03-unified-attestation-framework}
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\input{sections/04-job-container-format}
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\input{sections/05-gossip-based-job-distribution}
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\input{sections/06-economic-model}
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\input{sections/07-developer-experience}
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\input{sections/08-performance-analysis}
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\input{sections/09-security-analysis}
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\input{sections/10-related-work}
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\input{sections/11-conclusion-and-future-work}
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\input{sections/13-sgx-quote-format-details}
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\input{sections/14-amd-sev-snp-attestation-report}
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\input{sections/15-nvidia-gpu-tee-attestation}
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\input{sections/99-acknowledgments}
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\end{document}
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