benchmark design

Existing software modeling datasets are often ad hoc constructions lacking rigorous quality assurance, leading to research findings that are difficult to reproduce, compare, and prone to bias. This work proposes the first benchmarking framework specifically designed for model-driven engineering, treating datasets themselves as first-class evaluation targets. By defining clear metrics for quality, representativeness, and task suitability, the framework establishes a unified platform that enables automated analysis of modeling datasets across multiple languages and formats. For the first time, this approach facilitates systematic evaluation of modeling datasets, substantially enhancing the reproducibility, fairness, and scientific rigor of research in the field.

This work addresses the high cost of machine learning benchmarking by proposing a systematic framework to efficiently select small, representative subsets of datasets while preserving model ranking stability. The study presents the first comprehensive evaluation of various dataset selection strategies—including clustering, A/D-optimal experimental designs, random baselines, and a greedy farthest-first (FAFI) approach—on rank fidelity. It derives a theoretical upper bound on Spearman rank correlation error for FAFI and integrates bootstrap aggregation to yield statistically rigorous confidence intervals for comparing strategy performance. Empirical results demonstrate that as few as five datasets suffice to achieve 0.95 rank correlation in time series classification, significantly outperforming random selection in NLP tasks, though gains are limited in recommendation systems.

Traditional static test sets inadequately evaluate foundation models’ diverse capabilities in open-ended scenarios. To address this, we propose ONEBench—a dynamic, extensible benchmarking paradigm that enables on-demand generation of customized evaluation suites targeting open capabilities, framing model assessment as a collective selection and aggregation process over sample-level tests. Our key contributions include: (1) the first unified, open-ended, and evolvable evaluation framework operating at the sample level; (2) a sparse measurement aggregation algorithm, a progressive sample pool construction mechanism, and a cross-modal unified interface (ONEBench-LLM/LMM); and (3) a robustness-aware scoring model with theoretical guarantees on identifiability and fast convergence. Experiments show that ONEBench achieves ranking stability >0.98 under 95% measurement sparsity, reduces evaluation cost by 20×, and attains >0.98 correlation with mean-score rankings on homogeneous data—enabling unified, efficient, and reliable assessment of both language and multimodal models.

Existing evaluation of generative AI is hindered by the scarcity of high-quality benchmarks, whose manual construction is costly and time-consuming. Method: We propose the first automated benchmark construction framework powered by collaborative large language model (LLM) agents, decomposing benchmark creation into four sequential stages—planning, generation, verification, and evaluation—integrating task decomposition, agent coordination, human-in-the-loop feedback, and explicit constraint-satisfaction assessment. Contribution/Results: The framework significantly enhances data diversity and metric reliability. Leveraging it, we construct the first high-quality benchmark specifically targeting planning and constraint-satisfaction capabilities in text generation. We systematically evaluate seven state-of-the-art models, uncovering shared failure modes and fine-grained capability disparities. Our work establishes a scalable, reproducible paradigm for evaluating generative AI capabilities, advancing both benchmark methodology and empirical analysis.

This work addresses the proliferation of large language model (LLM) evaluation benchmarks, which has outpaced systematic assessment of their intrinsic quality. To this end, we propose Benchmark², a novel framework that establishes the first quantitative methodology for evaluating the reliability and validity of LLM benchmarks through three complementary metrics: cross-benchmark ranking consistency, discriminability score, and capability alignment bias. Empirical evaluation across 15 benchmarks and 11 LLMs demonstrates that Benchmark² not only reveals substantial quality disparities among existing benchmarks but also enables the construction of streamlined test sets that maintain high evaluative performance while significantly reducing assessment scale.

This study addresses a critical gap in existing tool-calling evaluation benchmarks: the lack of validation of the evaluators themselves, which risks conflating assessment artifacts with agents’ true capabilities. Through a systematic audit of four prominent benchmarks—BFCL v4, τ2-Bench, LiveMCPBench, and MCP-Atlas—the authors conduct expert review of 496 tasks, replicate experiments, and perform trajectory-level analysis, revealing an 18.5% disagreement rate between automated evaluators and human judgment. Notably, LiveMCPBench exhibits a score variance of up to 18.9 percentage points upon re-evaluation, sufficient to overturn leaderboard rankings. To address these issues, the work introduces the first unified taxonomy of tool-calling evaluation failures, advocates for distinct measurement of tool invocation, task completion, and result verification, and releases Tool-Veritas—a configurable benchmark—and Harness Lab, an open-source evaluation platform.

Current evaluations of AI models lack standardized protocols, with institutions selectively employing benchmarks in ways that hinder cross-study comparability and raise concerns about scientific validity. This work introduces Benchmarking-Cultures-25, a dataset encompassing 231 benchmarks from 139 model releases, and combines qualitative content analysis with a unified categorization framework to systematically expose the fragmentation in benchmark selection: 63.2% of benchmarks are used by only a single institution, and 38.5% appear just once. Moreover, many benchmarks marketed as “general-purpose” disproportionately emphasize STEM—particularly mathematics—while often neglecting construct validity. The study further proposes a taxonomy aligning ostensibly disparate terminologies to their underlying measurement signals and develops an interactive tool revealing that benchmarks frequently serve marketing narratives rather than rigorous scientific assessment.

Existing benchmarks for knowledge work evaluation largely adhere to traditional NLP task paradigms, failing to capture systems’ capabilities in real-world knowledge-intensive settings. This work proposes a three-step framework—explicitly defining work activities, establishing realistic test environments, and focusing evaluation on deliverable outputs—and derives 18 core knowledge work activities from the O*NET database. Innovatively integrating role responsibilities, local tool usage, and downstream usability into benchmark design, the approach establishes a coherent “work activity–test setup–scoring artifact” alignment. Validation through three case studies (GDPval, OfficeQA Pro, and APEX-SWE) exposes critical misalignments in current benchmarks between tasks, environments, and actual work objectives, offering a new paradigm for evaluating knowledge work systems in practical, application-oriented contexts.

Traditional benchmarks provide only aggregate scores, offering insufficient evidence to support reliable deployment decisions and thereby creating a disconnect between evaluation and action. To address this gap, this work proposes a “deployment-completeness” benchmarking framework, introducing novel metrics—evidence fibers, completeness curves, and certifiable proportions—alongside a systematic audit methodology comprising evidence fiber analysis, response ranking intervals, conformal coverage evaluation, and a certify-then-acquire decision pipeline. Empirical evaluation on benchmarks such as Tox21, Matbench, and JARVIS reveals that conventional approaches suffer a drastic drop in channel coverage to 10.07% under real-world deployment conditions. In contrast, the proposed method reduces error-driven deployment decisions to 0.027% on Tox21 and 0.128% on JARVIS, substantially enhancing deployment reliability.

This work addresses the challenge of systematically evaluating concept bottleneck models, whose applicability and failure mechanisms remain poorly understood due to the scarcity of real-world datasets with annotated concept labels. To bridge this gap, we introduce the first controllable synthetic benchmark that leverages parametric generation techniques to precisely modulate data modality, concept selection, annotation quality, and label completeness, thereby simulating diverse real-world relationships between concepts and predictions. This benchmark enables comprehensive evaluation of various concept bottleneck models across both decision-support and fully automated tasks, effectively identifying key performance determinants and characteristic failure modes. Our framework fills a critical void in the current evaluation landscape for concept-based interpretability methods.