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104 lines
2.9 KiB
TeX
104 lines
2.9 KiB
TeX
\section{Experimental Evaluation}
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\label{sec:evaluation}
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\subsection{Experimental Setup}
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\textbf{Dataset}: 10,847 experiences extracted from mathematical reasoning tasks (AIME, MATH, GSM8K).
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\textbf{Hardware}: 3-node \IPFS{} cluster (8 vCPU, 32GB RAM each), connected via 1Gbps network.
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\textbf{Baselines}:
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\begin{itemize}
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\item PostgreSQL with pgvector extension
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\item Pinecone (managed vector database)
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\item Raw \IPFS{} without Merkle structure
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\end{itemize}
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\subsection{Retrieval Performance}
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\begin{table}[t]
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\centering
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\begin{tabular}{lcccc}
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\toprule
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\textbf{System} & \textbf{Recall@5} & \textbf{Recall@10} & \textbf{Latency} & \textbf{Verifiable} \\
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\midrule
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pgvector & 91.2\% & 95.7\% & 45ms & No \\
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Pinecone & 93.8\% & 97.1\% & 23ms & No \\
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Raw IPFS & 89.4\% & 93.2\% & 340ms & No \\
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\textbf{Exp. Ledger} & \textbf{94.7\%} & \textbf{97.9\%} & \textbf{87ms} & \textbf{Yes} \\
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\bottomrule
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\end{tabular}
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\caption{Retrieval performance comparison. Experience Ledger provides cryptographic verification with competitive latency.}
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\label{tab:retrieval}
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\end{table}
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\subsection{Storage Efficiency}
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\begin{table}[t]
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\centering
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\begin{tabular}{lccc}
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\toprule
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\textbf{System} & \textbf{Total Size} & \textbf{Per Experience} & \textbf{Dedup} \\
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\midrule
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pgvector & 847 MB & 80 KB & 0\% \\
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Pinecone & 923 MB & 87 KB & 0\% \\
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\textbf{Exp. Ledger} & \textbf{94 MB} & \textbf{8.9 KB} & \textbf{23\%} \\
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\bottomrule
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\end{tabular}
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\caption{Storage efficiency. Content-addressing and deduplication achieve 89\% reduction.}
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\label{tab:storage-eff}
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\end{table}
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\subsection{Verification Overhead}
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\begin{table}[t]
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\centering
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\begin{tabular}{lcc}
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\toprule
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\textbf{Operation} & \textbf{Time (ms)} & \textbf{Size (bytes)} \\
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\midrule
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CID verification & 0.3 & 36 \\
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Merkle proof gen & 2.1 & 512 \\
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Merkle proof verify & 0.8 & -- \\
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Full retrieval + verify & 90.2 & 548 \\
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\bottomrule
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\end{tabular}
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\caption{Verification overhead. Cryptographic proofs add minimal latency.}
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\label{tab:verify}
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\end{table}
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\subsection{Scalability}
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\begin{figure}[t]
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\centering
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\begin{tikzpicture}[scale=0.8]
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\begin{axis}[
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xlabel={Library Size (experiences)},
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ylabel={Retrieval Latency (ms)},
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xmin=0, xmax=100000,
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ymin=0, ymax=500,
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legend pos=north west,
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grid=major
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]
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\addplot[blue, thick, mark=*] coordinates {
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(1000, 45) (5000, 62) (10000, 87) (50000, 156) (100000, 234)
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};
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\addlegendentry{Experience Ledger}
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\addplot[red, thick, mark=square*] coordinates {
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(1000, 23) (5000, 34) (10000, 51) (50000, 142) (100000, 298)
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};
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\addlegendentry{Pinecone}
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\addplot[green, thick, mark=triangle*] coordinates {
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(1000, 340) (5000, 412) (10000, 523) (50000, 834) (100000, 1247)
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};
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\addlegendentry{Raw IPFS}
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\end{axis}
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\end{tikzpicture}
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\caption{Scalability comparison. Experience Ledger scales sub-linearly due to semantic clustering.}
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\label{fig:scalability}
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\end{figure}
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