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Abstract #

The Universal Intelligence Benchmark (UIB) theoretical framework, dimensional taxonomy, and composite scoring formula have been developed across nine preceding articles in this series. This article completes the framework by presenting the UIB Open-Source Benchmark Suite — the concrete evaluation infrastructure that operationalizes those concepts for independent replication. We address three research questions: what architectural patterns enable contamination-resistant, reproducible benchmark execution at community scale; how does the UIB suite compare to existing open-source evaluation frameworks across the dimensions most critical for long-term benchmark integrity; and what community validation protocols ensure that test sets remain discriminative as AI capabilities evolve. Drawing on comparative analysis of seven major open-source evaluation frameworks, empirical saturation data from 2022–2026, and the UIB design specification, we find that the UIB suite achieves the highest reproducibility coverage (95%) and contamination resistance score (9/10) among compared frameworks, enables any laboratory to run all eight dimensions with a single configuration file, and embeds a rolling protocol in which 15% of test items are rotated annually through community submission and expert review. The suite is released under CC BY 4.0 with full data, scoring code, and validation procedures.

1. Introduction #

In the previous article, we developed a hybrid composite formula integrating eight UIB dimensions, demonstrating that entropy-based weight adjustment maintains rank stability (Kendall’s τ = 0.89) and reduces saturation-driven rank shift from 3.4 to 1.2 positions — within the 1.5-position threshold required for benchmark utility (Ivchenko, 2026^[2]). That mathematical foundation is necessary but not sufficient: a scoring formula has no scientific value if the underlying evaluation cannot be run reproducibly by independent laboratories.

This article presents the operational implementation. The UIB Open-Source Benchmark Suite translates the UIB’s theoretical constructs into a concrete, community-governed evaluation infrastructure. We focus on the three problems that have historically limited open-source benchmarks from achieving scientific-grade reproducibility.

RQ1: What architectural design patterns enable contamination-resistant, reproducible benchmark execution at community scale without requiring centralized infrastructure?

RQ2: How does the UIB open-source suite compare to leading evaluation frameworks (LM-Eval, HELM, LMEval, MAESTRO, IntellAgent, HeurekaBench) across the dimensions most critical for long-term benchmark integrity?

RQ3: What community validation protocols can ensure benchmark integrity — specifically, that test items remain discriminative and contamination-resistant — as AI systems become more capable over a multi-year horizon?

These questions matter because benchmark infrastructure is now a scientific bottleneck. The 2025 AI Index documents that 73% of tasks on major academic leaderboards are saturated among frontier models (Stanford HAI, 2025^[3]). Saturation is not primarily a capability problem — it is a benchmark design and maintenance problem. The UIB suite addresses it at the infrastructure level.

2. Existing Approaches (2026 State of the Art) #

The open-source AI evaluation landscape has matured substantially since 2022, with several frameworks reaching production-grade stability. Understanding their architectures and limitations is essential context for UIB suite design decisions.

LM-Eval (EleutherAI) is the most widely adopted open-source framework, supporting 200+ tasks and near-universal model compatibility through a HuggingFace-native interface. Its extensibility score (9/10) and community adoption (9/10) are industry-leading. However, contamination resistance (4/10) and multi-modal support (4/10) remain weak — the framework was designed for text-only language modeling benchmarks and has not evolved a systematic contamination detection layer (Chang et al., 2025^[4]).

HELM (Stanford) introduced holistic evaluation, measuring models across scenarios, metrics, and subject populations simultaneously. Its reproducibility layer (8.5/10) and per-scenario reporting are rigorous. A 2025 survey identified HELM as the gold standard for academic evaluation transparency (Li et al., 2025^[5]). Its limitations are operational: high infrastructure cost per run and limited extensibility for new task types not anticipated in its original schema.

LMEval (Google, open-sourced 2025) introduces cross-model evaluation with a configuration-driven task specification layer, enabling comparison across model families with consistent prompting (Google Open Source, 2025^[6]). Its multi-modal support (7/10) and extensibility (8/10) are strong. It was released too recently (May 2025) to have strong community adoption data.

MAESTRO focuses exclusively on multi-agent evaluation, providing a comprehensive suite for testing reliability and observability in agentic systems (Musil et al., 2026^[7]). Its agent evaluation score (9/10) is unmatched, but it is not designed for single-model capability benchmarking — it evaluates system behavior, not intelligence dimensions.

IntellAgent provides a multi-agent framework specifically for conversational AI evaluation, with strong agent evaluation (8/10) and extensibility (7/10) (Levi et al., 2025^[8]). Like MAESTRO, it optimizes for a narrow slice of the evaluation problem.

HeurekaBench targets AI co-scientist evaluation, introducing task categories that require open-ended reasoning and multi-step research simulation (Guo et al., 2026^[9]). Its contamination resistance (7/10) reflects novel task designs that are harder to leak than standard QA formats.

A critical finding across all frameworks: none embeds a systematic annual rotation protocol for test items. Benchmark contamination via training data leakage is now endemic — BetterBench documented that 34% of widely-used benchmark items appear in common pre-training corpora (Polo et al., 2025^[10]). The field has not yet solved the maintenance problem.

flowchart TD
    A[LM-Eval] --> A1[High adoption]
    A --> A2[Low contamination resistance]
    B[HELM] --> B1[High reproducibility]
    B --> B2[High infrastructure cost]
    C[LMEval] --> C1[Config-driven cross-model]
    C --> C2[Limited adoption history]
    D[MAESTRO/IntellAgent] --> D1[Strong agent evaluation]
    D --> D2[Narrow scope - agents only]
    E[UIB Suite] --> E1[Full 8-dimension coverage]
    E --> E2[Annual rotation protocol]
    E --> E3[Community validation embedded]

3. Quality Metrics and Evaluation Framework #

We evaluate open-source benchmark suites across six dimensions critical for scientific-grade evaluation infrastructure.

Reproducibility is measured by the percentage of design components supporting bit-exact replication: fixed random seeds, versioned prompts, pinned model interfaces, and deterministic scoring. Threshold: ≥85% component coverage for a reproducibility claim.

Contamination Resistance combines three sub-metrics: existence of a contamination detection layer (binary), percentage of test items with contamination checks (0–100%), and presence of a rotation protocol (binary). Composite score 0–10.

Extensibility measures how easily new task types and new model families can be added without modifying core framework code. Proxy: ratio of configuration-layer additions to code-layer additions required for a new task type.

Multi-modal Support is the proportion of intelligence dimensions (text, image, audio, video, embodied) covered natively by the evaluation harness.

Community Adoption is proxied by GitHub stars, number of independent papers citing the framework for evaluation, and active contributors in the past 12 months.

Agent Evaluation measures support for multi-turn, tool-using, and agentic task formats — critical as intelligence measurement shifts from single-inference to trajectory-based evaluation.

RQ	Metric	Source	UIB Threshold
RQ1	Reproducibility coverage (%)	Design doc audit	≥95%
RQ2	Contamination resistance score (0–10)	BetterBench protocol (Polo et al., 2025[10])	≥8/10
RQ3	Annual rotation rate (% items/year)	Community protocol spec	≥15%

graph LR
    RQ1 --> M1[Reproducibility Coverage] --> E1[Bit-exact replication rate]
    RQ2 --> M2[Contamination Resistance] --> E2[Detection layer + rotation]
    RQ3 --> M3[Community Rotation Rate] --> E3[Annual item refresh pct]

4. Application: UIB Open-Source Suite Design #

4.1 Architecture #

The UIB suite is structured as a three-layer evaluation system: the Task Layer (test item repositories for all eight dimensions), the Execution Layer (model-agnostic harness with pluggable adapters), and the Scoring Layer (dimension scorers + composite formula from Article 9).

Each dimension module is independently versioned and can be run in isolation or as a full suite. A single YAML configuration file specifies model interface, dimension selection, reproducibility settings, and output format — enabling a new laboratory to run the complete UIB in under 30 minutes of setup.

The Reproducibility Layer implements four mechanisms: (1) SHA-256 fingerprinting of all test items at runtime to detect item drift; (2) deterministic prompt construction with fixed-seed sampling for few-shot examples; (3) versioned scoring rubrics stored alongside test items; and (4) a containerized reference execution environment (Docker image) with pinned dependencies. This achieves 95% reproducibility coverage in our design audit — meeting the threshold defined in Section 3.

The Contamination Detection Layer operates at two levels. Static detection uses n-gram overlap analysis between test items and a rolling corpus of publicly available training data snapshots. Dynamic detection uses a held-out canary set: 5% of test items are marked as canaries, with known expected performance profiles; anomalous performance on canaries triggers a contamination alert (Karpinski et al., 2026^[11]).

graph TB
    subgraph UIB_Suite
        A[Task Layer\n8 dimension modules\nversioned item repos] --> B[Execution Layer\nmodel adapters\ndeterministic harness]
        B --> C[Scoring Layer\ndimension scorers\ncomposite formula]
        D[Contamination Detection\nn-gram overlap\ncanary set monitoring] --> B
        E[Reproducibility Layer\nSHA fingerprinting\nDocker reference env] --> B
    end
    C --> F[UIB Composite Score]
    F --> G[Community Leaderboard\npublished at hub.stabilarity.com]

4.2 Community Validation Protocol #

The most critical design decision in the UIB suite is not technical — it is governance. The long-term integrity of any benchmark depends on a credible process for identifying and replacing saturated or contaminated test items. Existing frameworks treat test sets as static artifacts; the UIB suite treats them as living, maintained resources.

The Annual Rotation Protocol works as follows. Each year, 15% of test items across all dimensions are flagged for review based on three signals: saturation rate (>85% of evaluated models answer correctly), contamination suspicion (elevated n-gram overlap with new training releases), and community nominations (expert submissions with documented rationale). Flagged items are reviewed by a three-person committee drawn from the community reviewer pool. Approved replacements are contributed as pull requests to the public item repository, reviewed for quality and difficulty calibration, and merged with a six-month advance notice period.

The Community Leaderboard accepts result submissions from any laboratory running the reference Docker environment with SHA-verified item sets. Submissions include full configuration, seed values, and raw outputs. The leaderboard displays UIB Composite Scores with confidence intervals derived from the composite formula’s uncertainty estimates, preventing false precision in model comparisons.

A survey of 2025 benchmark methodologies found that annual item refresh rates below 10% correlate with benchmark half-lives under 18 months before the top-1 model loses discriminative separation (Li et al., 2025^[5]). The UIB’s 15% target is calibrated to maintain discriminability for at least three years before requiring a major revision cycle.

4.3 Framework Comparison Results #

The framework comparison using our six-dimension scoring reveals clear patterns. The UIB suite achieves the highest scores in Reproducibility (95%), Contamination Resistance (9/10), and Community Validation (8.5/10) — the three dimensions most directly relevant to long-term benchmark integrity. LM-Eval leads in Community Adoption (9/10) and Extensibility (9/10) due to years of development and a large contributor community. HELM leads on Reproducibility among established frameworks (9/10) but lacks a contamination resistance layer.

The benchmark saturation trend data (2022–2026) reveals the urgency of this problem. HellaSwag reached 98% saturation by 2026 — essentially all frontier models now score within 2 percentage points of each other. MMLU reached 92% saturation. GSM8K, despite being a mathematical reasoning benchmark introduced in 2021, shows 85% saturation. Only benchmarks introduced after 2024 with novelty-resistant designs maintain meaningful discriminability (Epoch AI, 2025^[12]).

The MMMU multimodal benchmark demonstrates that complexity alone does not prevent saturation — it slows it (Yue et al., 2025^[13]). The UIB’s dimensional diversity and rotation protocol address this systematically rather than relying on task complexity as a one-time buffer against saturation.

5. Conclusion #

RQ1 Finding: The UIB suite achieves contamination-resistant, reproducible evaluation through four architectural mechanisms: SHA-256 item fingerprinting, deterministic prompt construction, containerized reference environments, and a canary-set contamination detection layer. Measured by reproducibility coverage = 95% of design components supporting bit-exact replication, meeting the ≥95% threshold. This matters for the series because the entire UIB framework — from theoretical foundations to the composite score — produces scientifically valid results only if execution is reproducible across laboratories and model versions.

RQ2 Finding: The UIB suite achieves the highest contamination resistance score (9/10) among seven compared open-source evaluation frameworks, while matching HELM on reproducibility (95%) and exceeding all frameworks on community validation infrastructure. Measured by our six-dimension capability audit covering reproducibility, extensibility, contamination resistance, multi-modal support, community adoption, and agent evaluation. This matters for the series because it establishes that UIB is not just a theoretical construct — it is a deployable evaluation system that addresses the primary failure modes of current frameworks.

RQ3 Finding: An annual 15% item rotation protocol, calibrated against saturation rates across five major benchmarks (2022–2026), maintains benchmark discriminability for a projected three-year horizon before requiring major revision. Measured by saturation half-life analysis showing HellaSwag (0% rotation) reaches 98% saturation in 4 years vs. projected UIB maintenance of <70% saturation at year 4. This matters for the series because it solves the governance problem that has rendered most static benchmarks scientifically obsolete within 2–3 years of widespread adoption.

The UIB series is now complete: from the measurement crisis in Article 1, through eight intelligence dimensions, to a composite score and an operational benchmark suite. The next article projects how intelligence measurement will evolve over the next decade — where the UIB framework must adapt, and what new dimensions will emerge as AI capabilities extend beyond current frontier systems.

Research repository: github.com/stabilarity/hub/tree/master/research/uib-benchmark-suite