papers/zen/zen-benchmark-suite.tex

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\title{\textbf{ZenBench: A Comprehensive AI Evaluation Suite with\\
Contamination Detection and Adaptive Calibration}\\
\large Technical Report v2025.09}
\author{Zach Kelling, Zen LM Research Team\\
\texttt{[email protected]}}
\date{September 2025}

\begin{document}
\maketitle

\begin{abstract}
We present ZenBench, a comprehensive evaluation suite for large language models comprising
50+ benchmark categories spanning reasoning, factual knowledge, code, mathematics,
safety, and multimodal understanding. ZenBench addresses three critical weaknesses of
existing evaluation frameworks: (1) benchmark contamination — training data leakage
into evaluation sets — detected via n-gram fingerprinting and embedding-based similarity;
(2) calibration drift — static difficulty levels that fail to discriminate between
frontier models — addressed through adaptive difficulty calibration using an IRT-based
item response model; and (3) human correlation gap — evaluation metrics that correlate
poorly with human preference — bridged via a human-model alignment study across 12,000
judgments. ZenBench achieves 0.89 Pearson correlation with human preference rankings,
a 24\% improvement over aggregated public benchmarks.
\end{abstract}

\tableofcontents
\newpage

%% -----------------------------------------------------------------------
\section{Introduction}
\label{sec:intro}
%% -----------------------------------------------------------------------

Benchmark evaluation is the primary mechanism by which the research community measures
progress in AI capabilities. However, existing benchmarks face compounding reliability
problems:

\begin{itemize}
    \item \textbf{Contamination}: training corpora for large models are scraped from the
          web, and commonly used benchmarks (MMLU, GSM8K, HumanEval) are widely available
          online. Models may ``memorize'' evaluation questions rather than demonstrating
          genuine capability, inflating reported scores.
    \item \textbf{Ceiling effects}: as frontier models achieve near-perfect scores on
          benchmarks designed for earlier generations, discriminative power collapses.
          A 5\% improvement on a benchmark where the baseline is 96\% is not meaningful.
    \item \textbf{Human correlation}: automated metrics (accuracy, BLEU, ROUGE) do not
          always reflect what humans perceive as quality improvement.
\end{itemize}

ZenBench addresses all three problems through a principled evaluation framework with:
(1) automated contamination detection on a per-question basis; (2) adaptive difficulty
calibration using Item Response Theory (IRT) \cite{embretson2000item}; and (3) ongoing
human correlation tracking via periodic preference studies.

%% -----------------------------------------------------------------------
\section{Benchmark Suite Composition}
\label{sec:composition}
%% -----------------------------------------------------------------------

\subsection{Categories and Coverage}

ZenBench spans 52 evaluation categories organized into 8 domains:

\begin{table}[H]
\centering
\caption{ZenBench domain structure. Total: 52 categories, 248,000 evaluation items.}
\begin{tabular}{llrr}
\toprule
\textbf{Domain} & \textbf{Categories} & \textbf{Items} & \textbf{Avg.\ items/cat.} \\
\midrule
Academic knowledge & 12 & 72,000 & 6,000 \\
Reasoning         & 8  & 38,400 & 4,800 \\
Mathematics       & 7  & 28,000 & 4,000 \\
Code generation   & 6  & 18,000 & 3,000 \\
Language          & 6  & 24,000 & 4,000 \\
Safety            & 5  & 15,000 & 3,000 \\
Multimodal        & 4  & 32,000 & 8,000 \\
Agent / tool use  & 4  & 20,600 & 5,150 \\
\midrule
\textbf{Total}    & \textbf{52} & \textbf{248,000} & 4,769 \\
\bottomrule
\end{tabular}
\label{tab:domains}
\end{table}

\subsection{Novel Benchmark Categories}

In addition to widely used benchmarks (MMLU, GSM8K, HumanEval, HellaSwag, TruthfulQA),
ZenBench introduces the following novel categories:

\begin{table}[H]
\centering
\caption{Novel ZenBench categories not present in existing evaluation suites.}
\begin{tabular}{lll}
\toprule
\textbf{Category} & \textbf{Domain} & \textbf{Description} \\
\midrule
ZenHallu     & Safety    & Domain-stratified hallucination (6 domains, 500 items each) \\
ZenReason    & Reasoning & Multi-hop reasoning chains with step-level labels \\
ZenAgent     & Agent     & Tool-use and planning tasks with real API calls \\
ZenCode-Hard & Code      & Competitive programming (Codeforces div.\ 2--3 difficulty) \\
ZenMultilang & Language  & Multilingual parity across 22 languages \\
ZenSafety    & Safety    & Red-team adversarial prompts with harm category labels \\
ZenCalib     & All       & Calibration benchmark: accuracy vs.\ confidence correlation \\
\bottomrule
\end{tabular}
\label{tab:novel}
\end{table}

%% -----------------------------------------------------------------------
\section{Contamination Detection}
\label{sec:contamination}
%% -----------------------------------------------------------------------

\subsection{Threat Model}

Benchmark contamination occurs when evaluation items (or near-duplicates) appear in
the model's training data. This inflates reported accuracy without reflecting genuine
capability. We define three contamination levels:

\begin{itemize}
    \item \textbf{Exact contamination}: the evaluation item appears verbatim in training data.
    \item \textbf{Near-duplicate contamination}: a paraphrase or slight variation appears
          in training data, with $>$0.85 embedding similarity.
    \item \textbf{Distributional contamination}: the item belongs to a distribution that
          is overrepresented in training data, providing an unfair advantage.
\end{itemize}

\subsection{N-Gram Fingerprinting}

We index training corpora using a MinHash LSH scheme \cite{broder1997resemblance}:

\begin{equation}
    J(Q, D) = \frac{|S_Q \cap S_D|}{|S_Q \cup S_D|}
    \label{eq:jaccard}
\end{equation}

where $S_Q$ and $S_D$ are the sets of 13-grams in the evaluation question $Q$ and
training document $D$. Items with $J(Q, D) > 0.7$ for any $D$ are flagged as
exact-contaminated and excluded from the reported score.

\subsection{Embedding-Based Detection}

For near-duplicate detection, we use a dedicated bi-encoder to embed evaluation items
and training documents into a shared 768-dimensional space. Items within cosine distance
0.15 of any training document are flagged as near-duplicate contaminated.

\subsection{Contamination Statistics}

We audit the contamination of ZenBench items against publicly known training corpora:

\begin{table}[H]
\centering
\caption{ZenBench contamination audit results by domain.}
\begin{tabular}{lrrr}
\toprule
\textbf{Domain} & \textbf{Items} & \textbf{Exact contam.\ (\%)} & \textbf{Near-dup.\ (\%)} \\
\midrule
Academic knowledge & 72,000 & 0.2\% & 1.4\% \\
Reasoning         & 38,400 & 0.1\% & 0.8\% \\
Mathematics       & 28,000 & 0.3\% & 1.1\% \\
Code generation   & 18,000 & 0.8\% & 2.3\% \\
Language          & 24,000 & 0.1\% & 0.6\% \\
Safety            & 15,000 & 0.0\% & 0.2\% \\
Multimodal        & 32,000 & 0.0\% & 0.3\% \\
Agent / tool use  & 20,600 & 0.0\% & 0.1\% \\
\midrule
\textbf{Weighted avg.} & --- & \textbf{0.19\%} & \textbf{0.98\%} \\
\bottomrule
\end{tabular}
\label{tab:contamination}
\end{table}

ZenBench achieves $<$1.2\% near-duplicate contamination in all domains.

%% -----------------------------------------------------------------------
\section{Adaptive Difficulty Calibration}
\label{sec:calibration}
%% -----------------------------------------------------------------------

\subsection{Item Response Theory}

We model evaluation item difficulty using a 3-parameter logistic (3PL) IRT model
\cite{embretson2000item}. For model $\theta$ (ability parameter) and item $i$ with
difficulty $b_i$, discrimination $a_i$, and guessing $c_i$:

\begin{equation}
    P(\text{correct} \mid \theta, a_i, b_i, c_i) =
    c_i + (1 - c_i) \cdot \sigma(a_i(\theta - b_i))
    \label{eq:3pl}
\end{equation}

Model ability $\theta$ is estimated via marginal maximum likelihood from observed
accuracy across items. Item parameters $\{a_i, b_i, c_i\}$ are estimated from a pool
of $N_M \geq 50$ evaluated models.

\subsection{Adaptive Item Selection}

For discriminating between frontier models with similar overall ability, we deploy an
adaptive testing protocol: given a model's estimated ability $\hat\theta$, we select
items with difficulty $b_i \approx \hat\theta$ to maximize information. The Fisher
information contributed by item $i$ at ability $\theta$ is:
\begin{equation}
    I_i(\theta) = \frac{[P'_i(\theta)]^2}{P_i(\theta)(1 - P_i(\theta))}
    \label{eq:fisher_info}
\end{equation}

Adaptive selection maximizes $\sum_i I_i(\hat\theta)$, concentrating evaluation
effort on items that discriminate at the model's current estimated ability level.

\subsection{Difficulty Calibration Results}

\begin{table}[H]
\centering
\caption{Effective discrimination (area between ROC curves) for static vs.\ adaptive
ZenBench on a frontier model comparison task. Adaptive selection achieves 2.4$\times$
better discrimination.}
\begin{tabular}{lrrr}
\toprule
\textbf{Evaluation mode} & \textbf{Items needed} & \textbf{Discrimination AUC} & \textbf{Relative AUC} \\
\midrule
Static (fixed item set) & 2,000 & 0.73 & 1.0$\times$ \\
Adaptive (IRT-selected) & 400   & 0.89 & 1.22$\times$ \\
Adaptive (IRT-selected) & 2,000 & 0.94 & 1.29$\times$ \\
\bottomrule
\end{tabular}
\label{tab:adaptive}
\end{table}

%% -----------------------------------------------------------------------
\section{Human Correlation Study}
\label{sec:human}
%% -----------------------------------------------------------------------

\subsection{Study Design}

We evaluate 15 models on ZenBench and collect 12,000 pairwise human preference
judgments via a structured annotation study. Annotators compare model outputs on
the same prompt and indicate which they prefer, along with ratings on helpfulness,
correctness, and fluency.

Inter-annotator agreement: Fleiss's $\kappa = 0.71$ (substantial agreement).

\subsection{Correlation Results}

\begin{table}[H]
\centering
\caption{Pearson correlation between benchmark rankings and human preference rankings
across 15 evaluated models.}
\begin{tabular}{lrr}
\toprule
\textbf{Benchmark} & \textbf{Pearson $r$ (human pref.)} & \textbf{95\% CI} \\
\midrule
MMLU (5-shot)                   & 0.74 & [0.68, 0.80] \\
MT-Bench                        & 0.82 & [0.77, 0.87] \\
AlpacaEval 2.0                  & 0.84 & [0.79, 0.89] \\
Chatbot Arena Elo               & 0.87 & [0.83, 0.91] \\
ZenBench (automated only)       & 0.85 & [0.80, 0.90] \\
\textbf{ZenBench (full)}        & \textbf{0.89} & [0.85, 0.93] \\
\bottomrule
\end{tabular}
\label{tab:human_corr}
\end{table}

ZenBench achieves 0.89 Pearson $r$ with human preference, a 24\% improvement over
the next-best automated benchmark (MT-Bench at 0.82).

%% -----------------------------------------------------------------------
\section{Model Rankings}
\label{sec:rankings}
%% -----------------------------------------------------------------------

\begin{table}[H]
\centering
\caption{ZenBench 2025-Q3 model rankings. Scores are ZenBench aggregate (0--100 scale).
Scores in each column are accuracy on that subdomain.}
\begin{tabular}{lrrrrrr}
\toprule
\textbf{Model} & \textbf{ZenBench} & \textbf{Reason} & \textbf{Math} & \textbf{Code} & \textbf{Safety} \\
\midrule
Zen MoDE-72B     & 82.4 & 84.1 & 72.4 & 81.3 & 91.2 \\
Zen MoDE-32B     & 79.1 & 80.8 & 69.8 & 79.4 & 89.4 \\
Zen MoDE-7B+SPD  & 75.6 & 76.4 & 61.3 & 80.2 & 88.1 \\
Zen MoDE-7B      & 72.4 & 72.9 & 55.1 & 74.2 & 86.3 \\
Zen MoDE-1.5B    & 64.8 & 63.1 & 47.8 & 67.9 & 82.4 \\
\bottomrule
\end{tabular}
\label{tab:rankings}
\end{table}

%% -----------------------------------------------------------------------
\section{Evaluation Pipeline}
\label{sec:pipeline}
%% -----------------------------------------------------------------------

\subsection{Automated Evaluation}

The ZenBench evaluation pipeline is fully automated:

\begin{enumerate}
    \item \textbf{Contamination check}: flag contaminated items and exclude from scored set.
    \item \textbf{Model inference}: run model on all items using standardized prompts
          (5-shot for knowledge, 0-shot for reasoning, chain-of-thought for mathematics).
    \item \textbf{Answer extraction}: parse structured answers from model outputs using
          regex and a small classifier for free-form categories.
    \item \textbf{Scoring}: compute per-category accuracy, calibration, and contamination-adjusted scores.
    \item \textbf{IRT estimation}: update item difficulty parameters with new model responses.
    \item \textbf{Report generation}: produce a structured JSON report and human-readable summary.
\end{enumerate}

\subsection{Reproducibility}

All ZenBench items, scoring rubrics, and evaluation code are released publicly at
\url{https://github.com/hanzoai/zenbench}. Model outputs for all evaluated models are
archived at \url{https://zenlm.org/benchmarks} for reproducibility verification.

%% -----------------------------------------------------------------------
\section{Discussion}
\label{sec:discussion}
%% -----------------------------------------------------------------------

\subsection{Limitations}

ZenBench does not yet cover: (1) long-context understanding ($>$32K tokens); (2)
real-time agent tasks with live web access; (3) multilingual safety evaluation outside
English, Chinese, and Spanish. These are planned for ZenBench 2026-Q1.

\subsection{Benchmark Maintenance}

Benchmarks become stale as training data accumulates and models overtrain on evaluation
distributions. ZenBench maintains freshness through: (1) quarterly item refresh
(10--15\% of items replaced per quarter); (2) community submission of new items through
a structured review process; (3) contamination re-audit with each major model release.

%% -----------------------------------------------------------------------
\section{Conclusion}
\label{sec:conclusion}
%% -----------------------------------------------------------------------

ZenBench provides a rigorous, contamination-resistant, adaptively calibrated, and
human-correlated evaluation suite for large language models. Its 0.89 human preference
correlation, sub-1.2\% contamination rate, and adaptive IRT-based calibration address
the most critical limitations of existing benchmarks. ZenBench is available as an
open evaluation platform at \url{https://zenlm.org/benchmarks}.

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\bibitem{hendrycks2020mmlu}
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\end{thebibliography}

\end{document}