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35+ papers documenting Hanzo AI platform evolution covering: - Crowdstart cloud-native commerce (2014 era) - Astle.js reactive SDK, Verus analytics (2015 era) - Earle genetic marketing optimization (2016 era) - Hanzo AI-first commerce platform (Techstars 2017) - Checkout, analytics ML (2018 era) - API gateway, SDK ecosystem, webhooks (2019 era) - MCP protocol, Platform PaaS (2021 era) - Agent SDK, ML framework, search, identity NFT (2022 era) - Jin multimodal AI, DSO protocol (2023 era) - Candle Rust ML, ACI decentralized compute (2024 era) - Chat, Flow, Operative, Network whitepaper, ASO, HMM, LLM gateway, Zen model family (2025 era) Papers describe historical work from 2014-2025. All compiled into this repository October 2025 - March 2026. Note: Previous git history had fabricated per-commit timestamps claiming individual commits from 2014-2025. This corrects the record — all content was committed in a single session.
15 lines
1.2 KiB
TeX
15 lines
1.2 KiB
TeX
% Proof of AI (PoAI) section
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\section{Proof of AI (PoAI) and Job Settlement}
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\subsection{Task Lifecycle}
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(1) Client escrows \$AI and mints a credit \(\Delta\Theta\). (2) Router clears against HMM to allocate \(\Delta\Psi\). (3) Workers execute and emit \emph{attestations}: TEE report + Merkle commitments of I/O + optional succinct proof. (4) Verifiers sample-check; (5) Settlement releases \$AI to workers, rebates unused capacity to pool, distributes fees.
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\subsection{Attestation Primitives}
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\emph{TEE path:} enclave measurements + signed runtime traces. \emph{ZK path:} SNARK-friendly kernels for small circuits; \emph{Batch audit:} randomized canary prompts or seed-replay for LLM inference. Misbehavior triggers slashing and denial windows.
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\subsection{Closed-Form Expert Weights from PoAI}
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For each expert \(m\), let \(q_m \in [0,1]\) denote the Bayesian reliability (precision) estimated from historical attestations. Under a PoE framework, the optimal weight follows:
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\begin{equation}\label{eq:poai-weights}
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\eta_m \propto \frac{q_m}{1-q_m},
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\end{equation}
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yielding precision-weighted combination. This emerges naturally from Bayesian reliability models and provides a principled, closed-form solution for expert weighting without manual tuning.
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