BenchBuilder: Modular Benchmark Engine

• BenchBuilder is a modular, automated engine for dynamic benchmark generation across varied domains, ensuring contamination resistance and robust evaluation.
• Its multi-stage pipeline transforms heterogeneous data sources into reproducible, configurable test suites via semantic chunking, model-based QA, and algorithmic filtering.
• Implementations like YourBench, ArenaBencher, and OSS-Bench demonstrate its versatility in enhancing evaluation for language models, code synthesis, workflow simulation, and numerical optimization.

BenchBuilder is a modular, automated benchmark generation engine designed to address limitations of traditional static datasets in LLM evaluation, code synthesis, workflow systems, and numerical optimization. It supports end-to-end workflows that transform heterogeneous data sources or existing benchmarks into robust, contamination-resistant, and highly configurable evaluation suites. Implementations span YourBench (Shashidhar et al., 2 Apr 2025), ArenaBencher (Liu et al., 9 Oct 2025), OSS-Bench (Jiang et al., 18 May 2025), WfBench (Coleman et al., 2022), GNBG (Yazdani et al., 2023), and the Arena-Hard pipeline (Li et al., 17 Jun 2024), all of which instantiate the BenchBuilder paradigm in their respective domains.

1. Theoretical Foundations and Motivations

BenchBuilder frameworks originated out of necessity for benchmarks that minimize saturation, contamination, manual curation overhead, and bias while reflecting new failure modes and domain relevance. Standard benchmarks—often static, ground-truth datasets—suffer rapid saturation as models match or exceed human-level performance, with data leakage from pretraining inflating scores and distorting model comparisons (Shashidhar et al., 2 Apr 2025, Liu et al., 9 Oct 2025, Li et al., 17 Jun 2024). BenchBuilder architectures directly confront these issues via dynamic, document-based or live task generation, grounding all evaluation data either in novel user-supplied corpora or crowd-sourced streams, and enforcing robust algorithmic and semantic checks throughout the pipeline.

The defining principles are:

• Contamination resistance through exclusive reliance on post-cutoff or user-supplied documents.
• Automated data generation, annotation, filtering, and judging.
• Cost-effectiveness, reproducibility, and adaptability to new domains or model paradigms.

2. Architecture and Pipeline Components

BenchBuilder implements a multi-stage pipeline with high modularity. The common canonical sequence encompasses:

1. Data Ingestion and Normalization: Conversion of arbitrary formats (PDF, HTML, Word, images) to a unified representation. For instance, ReaderLM-v2 and Markitdown standardize all source inputs to markdown (Shashidhar et al., 2 Apr 2025), while OSS-Bench parses OSS codebases using libclang-based AST traversal (Jiang et al., 18 May 2025).
2. Semantic Chunking / Task Extraction: Segmentation of input data into coherent units, leveraging embedding similarity thresholds and token length limits to delineate context windows or code snippets. Multi-hop chunking enables synthesis across segments for complex question types (Shashidhar et al., 2 Apr 2025, Coleman et al., 2022).
3. Benchmark Item Generation:
   • LLM-based QA Synthesis: Document-to-Evaluation Generation (D2EG), in which an ensemble of models is prompted over each context pair to yield candidate QA triples, maximizing diversity and coverage (Shashidhar et al., 2 Apr 2025).
   • Crowdsourced Task Selection: Embedding-based clustering (UMAP + HDBSCAN), LLM annotator-based skill scoring, and uniform sampling across clusters ensures topical diversity (Li et al., 17 Jun 2024).
   • Code Synthesis: Replacement of OSS functions using LLMs with controlled prompt patterns (Jiang et al., 18 May 2025).
4. Quality Filtering and Grounding Validation:
   • Citation matching via fuzzy Levenshtein partial ratios, retaining only those with citation grounding score above a threshold (Shashidhar et al., 2 Apr 2025).
   • Semantic deduplication via embedding clustering (DBSCAN), selection of medoid representatives, and weighting by cluster size (Shashidhar et al., 2 Apr 2025).
   • Algorithmic judging and intent verification (e.g., LLM-as-judge Likert scales and intent alignment checks) (Li et al., 17 Jun 2024, Liu et al., 9 Oct 2025).
5. Task Evolution and Difficulty Adjustment:
   • ArenaBencher’s iterative in-context prompting, multi-model aggregation of failure signals, and difficulty maximization (Liu et al., 9 Oct 2025).
6. Automated Evaluation and Metrics Computation:
   • Cost-controlled inference, bootstrapped accuracy intervals, separability, alignment, Brier scores, and fairness assessments (Li et al., 17 Jun 2024, Jiang et al., 18 May 2025, Liu et al., 9 Oct 2025).

Details for each step—including pseudocode—are explicitly documented in the targeted implementations.

3. Domain-Specific Implementations

BenchBuilder’s paradigm has generalized across multiple domains:

• LLM Benchmarking (YourBench, ArenaBencher, Arena-Hard): Dynamic extraction of QA pairs, factual, numeric, and multi-hop question synthesis, and citation validation for post-cutoff scientific corpora (e.g., Tempora-0325) (Shashidhar et al., 2 Apr 2025). Live crowd-sourced prompt selection and fully automated LLM-as-judge pairwise comparison (Li et al., 17 Jun 2024). Iterative benchmark evolution (ArenaBencher), driving difficulty and model separability (Liu et al., 9 Oct 2025).
• Software Engineering (OSS-Bench): Large-scale code benchmark generation from real OSS projects. Tasks: LLM-based function replacement, compilability checks, functional test suite pass rates, and sanitizer-based security checks (AddressSanitizer, FlowFusion) (Jiang et al., 18 May 2025).
• Scientific Workflow Simulation (WfBench): Synthesis of workflow DAGs via empirical subgraph recipes, tunable CPU/memory/I/O per task, and translation to major workflow systems (Pegasus, Swift/T, SciPipe) (Coleman et al., 2022).
• Numerical Optimization (GNBG): Parametric generation of single/multi-basin continuous functions, controlling multimodality, conditioning, separability, basin shapes, and deceptive traps via $\Theta$-parameter vectors (Yazdani et al., 2023).

4. BenchBuilder Metrics and Evaluation Protocols

BenchBuilder frameworks apply rigorously defined metrics reflecting separability, alignment, model ranking confidence, cost efficiency, and contamination resistance:

Example Table: Metrics Across Implementations

Framework	Separability Metric	Difficulty Control	Contamination Avoidance
YourBench	Spearman $\rho$	D2EG min-coverage constraint	Tempora-0325 post-cutoff
ArenaBencher	1 – max accuracy	Multi-model aggregation	Ability embedding, in-domain
Arena-Hard-Auto	Bootstrap CI overlap	LLM-skill scoring	Dedup + cluster sampling
OSS-Bench	Chained functional	Iteration sampling, fuzzing	Live OSS codebase evolution
WfBench	Makespan, ECDF match	DAG recipe, parametric stats	Empirical workflow mining
GNBG	Morphology control	Basin number/shape tuning	Single formula, new params

Each framework employs problem-specific metrics—e.g., functional correctness, sanitizer alerts, difficulty via multi-model failure rates, benchmark makespan, or morphological parameter sweeps—validated with both human raters and algorithmic checks.

5. Impact, Empirical Validation, and Limitations

BenchBuilder approaches have established significant advancements:

• Perfect preserved model ranking: Synthetic benchmarks achieve Spearman $\rho=1$ with original datasets, verified on seven MMLU subsets for eight LLMs, at cost under $15 (Shashidhar et al., 2 Apr 2025).
• Separability and human alignment: Arena-Hard-Auto achieves 3x tighter win-rate intervals than MT-Bench and 94.1% Spearman correlation with human rankings (Li et al., 17 Jun 2024).
• Code security challenge: OSS-Bench exposes 5–10x more sanitizer alerts in top LLM-generated code than the original baseline (Jiang et al., 18 May 2025).
• Scientific reproducibility: WfBench’s synthetic workflows match real-task makespans within 1–14% and accurately capture system overhead interactions (Coleman et al., 2022).
• Optimization landscape control: GNBG permits continuous modulation of multimodality, separability, and conditioning for systematic algorithmic testing (Yazdani et al., 2023).

Limitations include annotator and judge self-bias (LLMs rating their own outputs), topical selection bias, lack of robust multi-turn or multilingual benchmarks, verbosity bias, non-support for some language ecosystems, and compute time for large-scale synthesis.

6. API Usage and Extensibility

BenchBuilder systems expose Python APIs (e.g., yourbench.BenchBuilder (Shashidhar et al., 2 Apr 2025)), command-line interfaces (WfBench), or modular code templates. The workflow supports:

from yourbench import BenchBuilder builder = BenchBuilder( models=["qwen2.5-32b","Phi-4-14b","Claude-3.7-sonnet"], grounding_threshold=0.85, similarity_threshold=0.9, ) builder.add_documents(doc_paths=["astro1.pdf","astro2.html"]) benchmark = ( builder .preprocess() .generate_qa() .filter_and_dedup() .build_benchmark(format="multiple_choice") ) results = builder.evaluate(models_to_eval=["MyModel-v1"]) print(results.ranking, results.correlation)

Each step (preprocessing, QA generation, filtering, benchmark instantiation, evaluation) allows user-customized plug-ins for chunkers, embedding models, or evaluation routines. Results may be exported to JSON, CSV, or directly fed into automated judge ensembles.

7. Future Directions and Adaptation

BenchBuilder research is extending toward:

• Expansion of prompt skill taxonomies to include dialogue, creativity, safety, and internationalization (Li et al., 17 Jun 2024).
• Adaptive, active-learning-driven prompt or task selection for further difficulty tightening.
• Ensemble or “LLM-jury” judging to mitigate single-model bias.
• Integration of performance metrics (runtime, memory usage), static analysis, and multimodal task structures.
• Broader language and domain coverage, including support for non-compilation targets, non-English, or conversational agents.

BenchBuilder frameworks offer transparent, robust, and scalable methodologies for bespoke benchmark creation, evidencing rigorous model validation and supporting reproducible research across diverse computational domains.