BenchHub: Modular Benchmark Infrastructure

• BenchHub is a modular benchmark infrastructure that unifies heterogeneous datasets, standardized taxonomies, and automated pipelines for dynamic ML evaluation.
• It enables continuous updates and fine-grained sample annotation, allowing for scenario-specific and reproducible performance assessments.
• Its extensible design supports flexible API-driven filtering and reporting, facilitating transparent cross-domain model comparison.

BenchHub refers to a class of benchmark hubs or infrastructures integrating multi-domain evaluation datasets, unified taxonomies, automated pipelines, and tools for reproducible, scalable, and domain-aware performance assessment of machine learning systems. It is characterized by its modular architecture, support for continuous updates, fine-grained sample-level annotation, and public APIs for flexible filtering and reporting, in contrast to previous static or monolithic benchmarking suites. The BenchHub paradigm has been instantiated in LLM evaluation (Kim et al., 31 May 2025), high-performance computing (HPC) AI system benchmarking (Jiang et al., 2020), metamaterial property discovery (Chen et al., 8 May 2025), and hypergraph decomposition (Fischl et al., 2018), each targeting the reproducibility, extensibility, and customization demands of their respective domains.

1. Foundational Principles and Motivation

BenchHub frameworks address several limitations in the traditional benchmarking landscape. Prior benchmark collections, such as MMLU or MLPerf, were static, coarse-grained, and domain-restricted, with limited support for flexible, customizable evaluation. Datasets were scattered, heterogeneously formatted, and lacked unified taxonomies for skill, subject, or domain, impeding transparent cross-model and cross-domain analysis. BenchHub architectures are designed with the following principles:

• Unified Repository with Fine-grained Annotation: All evaluation samples are collected and normalized into a unified schema with sample-level metadata. For example, BenchHub for LLMs aggregates 303,000 questions across 38 benchmarks, each annotated with skill, subject, task type, and target labels (Kim et al., 31 May 2025).
• Extensible and Versioned Data Infrastructure: BenchHub implementations maintain a versioned store, supporting continuous addition, classification, and release cycles, ensuring reproducibility by tracking the exact dataset version and annotation state.
• Dynamic and Customizable Evaluation: APIs and command-line tools enable filtering and recomposing evaluation suites by domain, taxonomy, or other metadata, supporting scenario-specific or domain-aware benchmarking.
• Community Contribution and Transparency: Pipelines facilitate public submission of benchmark datasets and algorithmic contributions, while maintaining rigorous quality control and transparent experiment logs.

This organizational logic recurs in domains as diverse as LLM evaluation (Kim et al., 31 May 2025), AI system performance in HPC (Jiang et al., 2020), metamaterial discovery (Chen et al., 8 May 2025), and hypergraph decomposition (Fischl et al., 2018).

2. Data Ingestion, Taxonomy, and Versioning

BenchHub data pipelines are defined by a robust ingestion and normalization process:

• Automated Data Ingestion and Schema Harmonization: Data from disparate sources (e.g., Hugging Face, GitHub, institutional repositories) are automatically acquired and mapped to a unified schema using rule-based or LLM-guided agents. For LLM benchmarks, this includes transformation from heterogeneous JSON or CSV into standard JSONL with fields for ID, source, prompt, answer, category labels, and task type (Kim et al., 31 May 2025).
• Sample-level Ontology Assignment: Internal classification models (e.g., BenchHub-Cat-7B, a Qwen-2.5-7B variant) assign domain-specific taxonomies such as "skill" ($\in$ {knowledge, reasoning, value/alignment}), hierarchical "subject" (64 fine-grained categories under 6 coarse roots), "task type", and geographic "target". Assignment accuracy is reported (subject: 87%, skill: 96.7%, target: 49%).
• Version Control: All merged and updated datasets are versioned (vX.Y.Z), with the master dataset and schema available for reproducibility. Each version can be referenced in model evaluations, facilitating strict comparison and regression testing.

The following table summarizes key aspects of BenchHub data curation:

Operation	LLM BenchHub	MetamatBench	HyperBench
Source Count / Scale	38 / 303K samples	5 / 2M+ samples	3K+ hypergraphs
Sample-level Annotation	Skill, Subject	Lattice/unit-cell meta	Structural invariants
Ingestion	Automated, LLM	YAML configs, Python	Web+API, JSON
Versioning	Git, HF, per merge	Code/data snapshots	Data+engine logs

This data unification ensures traceability, allows reproducible subsetting, and underpins scenario-specific evaluation.

3. Benchmark Composition, Taxonomies, and Filtering

BenchHub supports multifaceted domain subdivision, taxonomic filtering, and test set customization.

• Taxonomic Structure: LLM BenchHub defines six coarse domains (Science, Technology, Humanities/Social Science, Arts & Sports, Culture, Social Intelligence) with up to 64 leaf labels (e.g., science/mathematics, technology/programming, HASS/history) (Kim et al., 31 May 2025).
• Sample-Level Task/Skill/Target Annotation: Each sample is tagged as, for example, "reasoning" skill, "programming" subject, and "local-KO" target, supporting fine control in test set construction.
• Scenario-based Customization: APIs allow users to construct, for example, a STEM-only multiple-choice test set, or perform evaluation restricted to cultural tasks in Korean, as demonstrated in provided code examples. A plausible implication is that domain-specific model audits or leaderboard recalculation can be achieved without re-running full benchmark suites.
• Cross-domain Transparency: By enabling per-category aggregation and reporting, BenchHub reveals domain-driven model performance heterogeneity (e.g., Llama-3.3-70B ranks 6th for Science & Tech but 1st for Culture & Social Intelligence (Kim et al., 31 May 2025)).

4. Metrics, Aggregation, and Evaluation Pipeline

BenchHub emphasizes rigorous, multi-granular performance assessment.

• Core Metrics: Each task and domain supports native metrics (accuracy, macro-averaged F$_1$, exact match, BLEU for generation), defined according to standard formulas. Derived metrics (e.g., time-to-quality, Valid FLOPS in HPC AI500 (Jiang et al., 2020)) may further penalize for failure to reach specified quality targets.
• Aggregation Strategies: Metrics are aggregated within and across categories. Weighted overall scores are computed as $Score_\mathrm{overall} = \sum_k w_k \cdot Metric_k$, where $w_k$ can reflect scenario priorities or sample distributions (Kim et al., 31 May 2025).
• Sampling and Ranking Sensitivity: Leaderboard positions depend on the sampling or category-weighting strategy. Simulations confirm significant rank swapping by scenario, underlining the need for transparent aggregation.
• Quality Audits and Error Robustness: Human-in-the-loop audits are carried out periodically to maintain taxonomy label integrity. Error rates up to 1.5% in label assignment do not materially affect model rankings.

For scientific computing, HPC AI500 provides metrics such as ValidFLOPS: $$\mathrm{ValidFLOPS} = \mathrm{FLOPS} \times \left(\frac{Q_{\text{achieved}}}{Q_{\text{target}}}\right)^n$$ with $n$ configurable by use case (Jiang et al., 2020).

5. System Architecture, APIs, and Extensibility

BenchHub deployments exhibit several infrastructural features supporting extensibility and integration:

• Backend / Frontend Separation: Typical stacks use Python microservices (Flask, FastAPI), standardized schema databases (HDF5, SQLite, PostgreSQL), and frontends in React.js or web-based APIs for user interaction (Chen et al., 8 May 2025, Fischl et al., 2018).
• Public APIs and CLI Tools: BenchHub exposes Python and command-line interfaces. Users can filter, evaluate, and report on custom subsets with minimal code, supporting both interactive and automated workflows.
• Continuous Update and Community Contribution: New datasets or models are added via web forms or pull requests. Automated pipelines handle ingestion and normalization, while maintainers review and merge contributions. The infrastructure is designed to withstand moderate mislabel rates and provide instant reproducibility.
• Modularity and Integration: New metrics, decomposition engines (HyperBench), or machine learning models (MetamatBench) are added by registering new modules and configurations. Workflows support dynamic picking up of new evaluation components without architectural overhaul.

6. Empirical Impact and Comparison with Prior Benchmarks

BenchHub implementations have demonstrated several empirical advantages:

• Transparent Domain-dependent Model Ranking: Experiments reveal substantial variation in LLM performance across knowledge domains, often producing rank swapping among top models depending on the evaluation scenario (Kim et al., 31 May 2025).
• Reproducibility and Scenario-driven Reporting: All evaluation results are versioned and tied to specific schema and annotation state, facilitating meaningful longitudinal and regression analyses.
• Holistic and Modular Extension: Diverse research communities (LLMs, HPC, metamaterials, hypergraphs) utilize BenchHub infrastructures to enable head-to-head method comparison, tailorable evaluation, and rapid adoption of new benchmarks or algorithms (Jiang et al., 2020, Chen et al., 8 May 2025, Fischl et al., 2018).
• Comparison to Prior Work: Unlike monolithic suites (MMLU, MLPerf), BenchHub maintains sample-level annotation, supports continuous ingestion, and enables domain/subdomain filtering. This suggests improved adaptability to rapidly evolving research objectives and more transparent benchmarking.

7. Prospects and Domain-generalization

BenchHub's pattern—unified, schema-rich repositories with dynamic, transparent, and customizable evaluation and extensibility via APIs—has emerged as a critical infrastructure across domains where dataset fragmentation or task heterogeneity impedes reproducibility and rigorous comparison. The approach scales from LLM evaluation (Kim et al., 31 May 2025) to AI systems benchmarking (Jiang et al., 2020), scientific discovery (Chen et al., 8 May 2025), and algorithmic toolkits (e.g., hypergraph decomposition (Fischl et al., 2018)), and can be plausibly generalized to other domains with rapidly evolving or diversifying evaluation landscapes.

BenchHub's integration of automation, reproducibility, and modularity addresses the core requirements for contemporary benchmarking in both academic and industrial contexts.