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zeekay
abf99d05fc
Restructured from flat .tex/.pdf files into per-paper subdirectories. Added Antje Worring as co-author on all papers. Synced PDFs to pdfs/ for download site.
140 lines
3.8 KiB
TeX
140 lines
3.8 KiB
TeX
\section{Consensus Engines}
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\subsection{Photon: Sampling-Based Consensus}
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Binary consensus using network sampling:
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\begin{algorithmic}[1]
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\State \textbf{function} \textsc{QueryRound}(preference, validators)
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\State $sample \gets \text{RandomSample}(validators, K)$
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\State $votes \gets \text{QueryPreference}(sample)$
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\If{$votes \geq \alpha$}
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\State $confidence \gets confidence + 1$
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\If{$confidence \geq \beta$}
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\State \Return $\texttt{FINALIZED}$
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\EndIf
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\Else
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\State $confidence \gets 0$
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\EndIf
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\State \Return $\texttt{CONTINUE}$
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\State \textbf{end function}
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\end{algorithmic}
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\textbf{Parameters:}
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\begin{itemize}
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\item $K = 25$: Sample size
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\item $\alpha = 15$: Quorum threshold
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\item $\beta = 20$: Confidence threshold
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\end{itemize}
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\subsection{Wave: Thresholding Consensus}
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Fast finality through adaptive thresholding:
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\begin{algorithmic}[1]
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\State \textbf{function} \textsc{WaveQuery}(choices, validators)
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\State $sample \gets \text{RandomSample}(validators, K)$
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\State $votes \gets \text{QueryPreferences}(sample, choices)$
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\For{each $choice \in choices$}
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\If{$votes[choice] \geq \alpha$}
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\State $preferences[choice] \gets preferences[choice] + 1$
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\If{$preferences[choice] \geq \beta$}
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\State \Return $choice$ \Comment{Finalized}
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\EndIf
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\EndIf
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\EndFor
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\State \Return $\texttt{CONTINUE}$
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\State \textbf{end function}
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\end{algorithmic}
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\subsection{Nova: DAG Finalizer}
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Finalizes transactions in DAG structures using Verkle proofs:
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\begin{algorithmic}[1]
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\State \textbf{function} \textsc{FinalizeVertex}(vertex, dag)
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\State $proof \gets \text{GenerateVerkleWitness}(vertex)$
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\If{$\text{ValidateWithWitness}(vertex, proof)$}
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\State $dag.\text{Finalize}(vertex)$
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\State \Return $\texttt{TRUE}$
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\EndIf
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\State \Return $\texttt{FALSE}$
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\State \textbf{end function}
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\end{algorithmic}
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\textbf{Verkle Tree Benefits:}
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\begin{itemize}
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\item $O(\log n)$ proof size vs. $O(n)$ for Merkle trees
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\item Constant-time verification
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\item Efficient state witness generation
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\end{itemize}
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\subsection{Nebula: Full DAG Consensus}
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Complete DAG consensus with parallel transaction processing:
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\begin{algorithmic}[1]
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\State \textbf{function} \textsc{ProcessTransaction}(tx, dag)
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\State $witness \gets witnessCache.\text{Get}(tx.ID)$
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\If{$verkleTree.\text{ValidateWithWitness}(tx, witness)$}
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\State $dag.\text{AddVertex}(tx)$
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\State $\text{Broadcast}(tx)$ \Comment{Parallel propagation}
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\State \Return $\texttt{TRUE}$
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\EndIf
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\State \Return $\texttt{FALSE}$
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\State \textbf{end function}
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\end{algorithmic}
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\subsection{Prism: Voting-Based Consensus}
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Direct voting for governance operations:
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\begin{algorithmic}[1]
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\State \textbf{function} \textsc{ProcessVote}(vote, proposal)
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\State $votes[proposal][vote.NodeID] \gets vote$
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\State $support \gets \text{CalculateSupport}(proposal)$
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\If{$support \geq threshold$}
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\State $\text{ExecuteProposal}(proposal)$
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\State \Return $\texttt{APPROVED}$
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\EndIf
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\State \Return $\texttt{PENDING}$
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\State \textbf{end function}
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\end{algorithmic}
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\subsection{Quasar: Quantum-Secure Overlay}
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The pinnacle layer adding triple-proof finality:
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\begin{algorithmic}[1]
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\State \textbf{function} \textsc{FinalizeBlock}(block)
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\State Launch $\text{CollectBLS}(block)$ in parallel
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\State Launch $\text{CollectCorona}(block)$ in parallel
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\State Wait for both with timeout = 10ms (GPU BLS) / 100ms (CPU fallback)
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\If{both certificates valid}
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\State \Return $\text{TripleProof}(blsCert, rtCert)$
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\Else
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\State \Return $\texttt{TIMEOUT}$ \Comment{Retry collection}
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\EndIf
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\State \textbf{end function}
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\end{algorithmic}
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