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Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation
Philipp D. Siedler, Jordan Sassoon
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Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 94%
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Summary
The paper introduces a dataset-centric meta-evaluation framework that audits benchmark datasets at the sample level across five latent dimensions: Cognitive and Knowledge Demands, Language and Content Quality, Task Properties, Context, and Ethics, Safety, and Fairness. By annotating five influential benchmarks (MMLU, ARC, WinoGrande, HellaSwag, and TruthfulQA), the authors reveal internal heterogeneity obscured by aggregate accuracy scores. The framework enables criterion-driven orchestration of composite benchmark subsets to support targeted evaluation of specific model capabilities like Reasoning Depth or Ethical Sensitivity.
Entities (16)
Relation Signals (15)
Framework → usesdimension → Cognitive and Knowledge Demands
confidence 98% · audits benchmark datasets at the sample level along five latent dimensions: 1. Cognitive and Knowledge Demands
Framework → usesdimension → Language and Content Quality
confidence 98% · audits benchmark datasets at the sample level along five latent dimensions: 2. Language and Content Quality
Framework → usesdimension → Task Properties
confidence 98% · audits benchmark datasets at the sample level along five latent dimensions: 3. Task Properties
Framework → usesdimension → Context
confidence 98% · audits benchmark datasets at the sample level along five latent dimensions: 4. Context
Framework → usesdimension → Ethics, Safety, and Fairness
confidence 98% · audits benchmark datasets at the sample level along five latent dimensions: 5. Ethics, Safety, and Fairness
MMLU → auditedby → Framework
confidence 95% · Applying this framework, we annotate five influential benchmarks -- MMLU...
ARC → auditedby → Framework
confidence 95% · Applying this framework, we annotate five influential benchmarks -- MMLU, ARC...
WinoGrande → auditedby → Framework
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Abstract
Abstract:Benchmark datasets are central to evaluating Large Language Models (LLMs), yet they are typically conceived as monolithic tasks, obscuring substantial variation in the demands of individual samples. We introduce a dataset-centric meta-evaluation framework that audits benchmark datasets at the sample level along five latent dimensions: 1. Cognitive and Knowledge Demands, 2. Language and Content Quality, 3. Task Properties, 4. Context, and 5. Ethics, Safety, and Fairness. Applying this framework, we annotate five influential benchmarks -- MMLU, ARC, WinoGrande, HellaSwag, and TruthfulQA -- revealing pronounced internal heterogeneity that is not captured by aggregate accuracy scores. We show how these annotations enable criterion-driven orchestration of composite benchmark subsets across datasets, supporting targeted evaluation of model capabilities such as Reasoning Depth or Ethical Sensitivity. This approach reframes benchmark evaluation as dataset introspection, providing a principled methodology for analyzing and re-composing existing benchmarks to better reflect diverse evaluation needs.
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- Source: https://arxiv.org/abs/2607.28801v1
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Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Philipp D. Siedler 1 Jordan Sassoon 1 Abstract Benchmark datasets are central to evaluating Large Language Models (LLMs), yet they are typically conceived as monolithic tasks, obscur- ing substantial variation in the demands of indi- vidual samples. We introduce a dataset-centric meta-evaluation framework that audits benchmark datasets at the sample level along five latent di- mensions: 1. Cognitive and Knowledge Demands, 2. Language and Content Quality, 3. Task Prop- erties, 4. Context, and 5. Ethics, Safety, and Fair- ness. Applying this framework, we annotate five influential benchmarks – MMLU, ARC, Wino- Grande, HellaSwag, and TruthfulQA – revealing pronounced internal heterogeneity that is not cap- tured by aggregate accuracy scores. We show how these annotations enable criterion-driven orches- tration of composite benchmark subsets across datasets, supporting targeted evaluation of model capabilities such as Reasoning Depth or Ethi- cal Sensitivity. This approach reframes bench- mark evaluation as dataset introspection, provid- ing a principled methodology for analyzing and re-composing existing benchmarks to better re- flect diverse evaluation needs. 1. Introduction Benchmark datasets have long served as the cornerstone of progress in Natural Language Processing (NLP) and lan- guage model development. From early milestones such as ARC (AI2 Reasoning Challenge) (Clark et al., 2018), which revealed the limits of shallow text-matching approaches, to broad evaluations like MMLU (Massive Multitask Lan- guage Understanding) (Hendrycks et al., 2021), benchmarks have enabled researchers to measure model and system per- formance in a standardized way. They are deeply embedded 1 Aleph Alpha Research, Heidelberg, Germany. Correspon- dence to: Philipp D. Siedler<philipp.siedler@aleph-alpha- research.com>. Preprint. August 3, 2026. in the research ecosystem, shaping not only scientific ad- vancements but also public perception of model capabilities. Despite their central role, benchmarks are often conceived as static instruments that yield a single score or leaderboard ranking. Such evaluations prioritize whether models com- plete the benchmark task correctly, while paying little at- tention to the internal properties of benchmark samples themselves. This perspective overlooks the nature of bench- marks: they are a non homogeneous collection of items. The contained samples often differ significantly on reason- ing depth, linguistic clarity, contextual framing, or ethical sensitivity (see, e.g. RACE (Lai et al., 2017) on reasoning depth; ERASER (DeYoung et al., 2020) on content qual- ity and evidence; Baldini et al. (2024) on variation across bias benchmarks; Scruples (Scarselli et al., 2009) on ethical sensitivity). Ignoring this diversity risks flattening com- plex evaluation signals into one-dimensional metrics such as exact match accuracy. Consider, for example, an ARC item that requires apply- ing background knowledge of heat transfer. A model may answer correctly through pattern recognition rather than causal reasoning, or fail despite demonstrating partial under- standing – the challenge lies not in the task label, but in the reasoning demand. In WinoGrande (Sakaguchi et al., 2019), pronoun resolution can hinge on cultural priors or gender stereotypes; in HellaSwag (Zellers et al., 2019), success de- pends on commonsense inference against carefully designed distractors; in TruthfulQA (Lin et al., 2022), responses must resist reproducing folk beliefs or misinformation; and in MMLU, performance varies widely across subjects, reveal- ing domain-specific blind spots. These cases illustrate that benchmark samples embody hidden dimensions – cognitive demands, linguistic precision, contextual assumptions, and ethical framing – that strongly influence model behavior but remain invisible to standard evaluation practice. In this work, we introduce a meta-evaluation framework that makes these hidden dimensions explicit. Our frame- work audits benchmark datasets at the sample level along five dimensions: 1. Cognitive and Knowledge Demands, 2. Language and Content Quality, 3. Task Properties, 4. Context, and 5. Ethics, Safety, and Fairness. We apply this framework to five influential benchmarks – MMLU, ARC, 1 arXiv:2607.28801v1 [cs.CL] 30 Jul 2026 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Table 1. Overview of five influential benchmarks considered in this study. The response type for selected benchmarks is loglikelihood. BenchmarkSample CountTask FormatDomain ARC3.55kMultiple-choice science exam questionsGrade-school science reasoning HellaSwag10kNarrative completion (plausible continuation)Commonsense inference, everyday scenarios MMLU15.9kMultiple-choice across 57 subjectsAcademic and professional knowledge TruthfulQA817Open-ended QA (factuality/misconceptions)General knowledge, misinformation and safety WinoGrande1.27kPronoun resolution (adversarial filtering)Commonsense reasoning, coreference WinoGrande, HellaSwag, and TruthfulQA – and generate structured annotations that characterize every sample. Cru- cially, we then leverage these annotations to re-sample new composite benchmarks across datasets, assembling targeted subsets that isolate and combine specific dimensions such as Reasoning Depth or Ethical Sensitivity. This enables us to compare LLMs not only by overall task accuracy, but also by their performance on newly surfaced evaluation criteria, revealing potential trade-offs that standard scores hide. While our analysis suggests the possibility of co-evolving benchmarks and models, this work focuses exclusively on evaluation: we audit and reinterpret existing benchmark datasets rather than proposing new dataset generation or col- lection methodologies. Rather than conceiving benchmarks as monolithic tasks, we reconceptualize them as latent multi- dimensional objects whose internal structure can be audited, interpreted, and re-composed for targeted evaluation. Our contributions are threefold: 1.We argue that benchmark datasets are latent multi- dimensional objects rather than monolithic tasks, and introduce a meta-evaluation framework that ex- poses hidden cognitive, linguistic, contextual, task, and ethical dimensions at the sample level. 2. Using this framework, we conduct a large-scale audit of five influential benchmarks, producing a publicly available annotated resource that reveals substantial internal heterogeneity as well as similarities across benchmarks. 3. We demonstrate how these latent dimensions can be operationalized to orchestrate composite evaluation subsets across datasets, enabling fine-grained and in- terpretable comparisons of LLM performance beyond aggregate accuracy. 2. Background Over the past decade, a few key benchmarks have shaped how researchers and the public assess model capabilities. These datasets differ in structure, coverage, and intent, each encoding assumptions about what counts as “success”. They also vary in cognitive demands, linguistic clarity, contextual framing, and ethical sensitivity. Below, we briefly introduce five influential benchmarks that underpin our study, before situating our work in the broader context of meta-evaluation. MMLU has become a de facto standard for measuring general knowledge in LLMs. It comprises multiple-choice questions across 57 subjects, from high-school to profes- sional expertise. Its breadth and perceived rigor make it popular, though critics note that many items are ambiguous, culturally specific, or solvable by recall rather than reason- ing (Singh et al., 2025; Salido et al., 2025; McIntosh et al., 2025). ARC tests reasoning beyond surface-level text matching through grade-school science questions requiring multi-step inference and world knowledge. It motivated research on combining language models with external reasoning tools, though its small scale and multiple-choice format limit its discriminative power (Moskvichev et al., 2023). WinoGrandeextends the Winograd Schema Challenge to test commonsense reasoning via pronoun resolution, using adversarial filtering to reduce annotation bias. While it helped explore model reliance on shallow cues, analyses reveal persistent gender and cultural biases (Sakaguchi et al., 2019; Zhao et al., 2018; Hansson et al., 2021). HellaSwagevaluates commonsense inference through nar- rative completion, requiring models to choose the most plau- sible continuation among fluent distractors. Although it effectively challenges shallow heuristics, concerns persist about validity and data contamination (Zellers et al., 2019; Chizhov et al., 2025; Li et al., 2024). TruthfulQA measures a model’s tendency to reproduce misconceptions or misinformation. Its open-ended ques- tions emphasize factual accuracy and epistemic caution, foregrounding safety and trustworthiness while revealing the difficulties of consistent human judgment. Together, these benchmarks reflect the diversity – and limi- tations – of current evaluation practice. Some target knowl- edge breadth, others commonsense or factual reliability, yet 2 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation all tend to collapse into single performance scores. Meta- evaluation reframes this focus, examining how dataset de- sign, linguistic quality, ethical framing, and contextual rele- vance shape outcomes, revealing model strengths and weak- nesses. 3. Related Work Meta-Evaluation of Benchmarks Beyond comparing models, several studies evaluate benchmarks themselves – meta-evaluation. Dynabench introduced human-and-model- in-the-loop data collection to reveal weaknesses in static leaderboards (Kiela et al., 2021). BIG-Bench – spanning over 200 tasks – captured both smooth scaling trends and sudden reasoning breakthroughs, while showing that so- cial biases can intensify with model size (Srivastava et al., 2022). Recent work broadens meta-evaluation: Subramo- nian et al. (2025) analyze misgendering benchmarks and find that probability-based and generation-based metrics often diverge, questioning metric alignment. Neplenbroek et al. (2024) introduce MBBQ, a multilingual extension of BBQ (Parrish et al., 2022), to test how generative LLMs express stereotypes across languages while controlling for culture and task effects. These efforts show that benchmark design and evaluation methodology jointly shape the signals interpreted as model ability. Dataset Audits and Artifacts Audits expose biases and spurious cues in common datasets. Gururangan et al. (2018) showed that SNLI (Bowman et al., 2015) and MultiNLI (Williams et al., 2018) contain annotation artifacts enabling label prediction from the hypothesis alone. Selvam et al. (2023) find that small construction choices in social bias benchmarks can alter measured bias or reverse rankings. Other analyses reveal cultural and distributional skew, with English-centric sources overstating generalization (Yang et al., 2025). Such findings highlight the need for system- atic, sample-level analysis rather than reliance on aggregate scores. Beyond Accuracy Evaluation frameworks increasingly move beyond accuracy. CheckList treats evaluation as be- havioral testing, uncovering failures via capability-based test matrices (Ribeiro et al., 2020). HELM expands evalu- ation into a multi-metric framework including calibration, robustness, fairness, and toxicity (Liang et al., 2023). Com- plementary work questions measurement validity: Hu & Levy (2023) show that prompt-based accuracy can diverge from probability-based knowledge estimates, while Elango- van et al. (2024) find that human uncertainty inflates metric correlations. Together, these works advocate evaluations that reveal data properties and quantify uncertainty rather than relying on a single correctness score. Unlike model- centric frameworks such as HELM (Liang et al., 2023) and behavioral test suites like CheckList (Lee et al., 2025), our approach is data-centric: we analyze benchmark datasets at the sample level to uncover latent structure that can be recombined into targeted evaluations across existing tasks. Ethical and Bias Considerations Benchmarks embed normative choices. Datasets such as WinoGender and Stere- oSet expose stereotypes, though results depend heavily on construction (Rudinger et al., 2018; Nadeem et al., 2020; Selvam et al., 2023). Cross-lingual audits like MBBQ show that stereotype patterns vary across languages even when cultural and task factors are controlled. These studies un- derscore that fairness, framing, and linguistic diversity are inseparable from evaluation design. Evaluator Models and LLM-as-a-Judge A growing di- rection adapts LLMs into evaluators. Rather than relying only on humans or static metrics, large models such as GPT-4 (OpenAI et al., 2024) are prompted or fine-tuned to provide rubric-based judgments of coherence, factuality, and harmlessness (Liu et al., 2023). Early studies show GPT-4 approximates expert evaluation across generation tasks (Bai et al., 2022; Liu et al., 2023; Zheng et al., 2023). Specialized approaches formalize the LLM-as-a-judge paradigm: Zheng et al. (2023) demonstrate consistent dialogue evaluation, Liang et al. (2023) use model-based scoring for summariza- tion and QA, and Chern et al. (2024) propose ScaleEval, an agent-debate framework for scalable meta-evaluation. Elangovan et al. (2024) further show that human label un- certainty limits evaluator-model correlations. Open-source alternatives such as Prometheus train smaller models on rubric-based feedback to achieve near-human agreement and rival GPT-4 (Kim et al., 2024). These strands of research show that evaluation is both techni- cal and normative. Dataset audits expose fragile benchmark signals; meta-evaluation projects such as Dynabench, BIG- Bench, and MBBQ reveal how benchmarks steer commu- nity focus; frameworks like HELM and CheckList propose richer criteria; and evaluator models such as Prometheus and ScaleEval demonstrate scalable, nuanced judgment. Our work extends this trajectory by cataloguing sample-level criteria that expose hidden dataset dimensions and by using these to construct composite benchmarks for targeted LLM evaluation beyond standard task accuracy. 4. Methodology To audit benchmarks at the sample level, we introduce a Catalogue of Criteria (Table 2), organized hierarchically into Dimensions, Aspects, and Indicators. Dimensions capture broad perspectives (e.g. Cognitive and Knowl- edge Demands or Task Properties), Aspects group related concerns within a Dimension, and Indicators are concrete, 3 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Table 2. Merged Catalogue of Criteria Dimensions, Aspects and Indicators (Sample-level Meta-data). DimensionAspectIndicatorValues Cognitive & KnowledgeReasoningReasoning Depth[0, 1, 2, 3] DemandsReasoning Type[causal, temporal, counterfactual, abductive, analogical, symbolic] KnowledgeKnowledge Type[common, specialized, scientific, numerical, cultural, narrative] Fact Recall[True, False] Narrative Understanding[True, False] Age Appropriateness[Elementary, Secondary, Undergraduate, Postgraduate] Language & ContentClarity & ReadabilityLanguage Difficulty[0, 1, 2, 3] QualitySpelling[0, 1, 2] Grammar[0, 1, 2] Referential Clarity[0, 1, 2, 3] Ambiguity Level[0, 1, 2, 3] Readability[0, 1, 2, 3] TruthfulnessFactual Accuracy[Correct, Dubious, Incorrect] Fact Checking Requirement[True, False] Verifiability[Yes, Partial, No] Task PropertiesStructureAnswerability[Yes, Partial, No] Label Quality[Correct, Dubious, Incorrect] MCQ Distractor Quality[0, 1, 2, 3] Temporal Sensitivity[True, False] Provenance / Leakage Risk[Low, Medium, High] ContextDomainTopical Domain[Math, Computer Science, Physics, Chemistry, Biology, Medicine, Engineering, Literature, History, Philosophy, Arts / Music, Economics, Psychology, Sociology, Political Science, Law, Business / Finance, Education / Exams, Technology / Internet, Everyday Knowledge, Pop Culture / Entertainment, Cultural / Religious Knowledge, News / Current Events, General Trivia, Other] Ethics, Safety &Ethical SignalsBias & Stereotyping[0, 1, 2, 3] FairnessCultural / Political Framing[True, False] Misinformation Bait[0, 1, 2, 3] Safety-Critical Relevance[True, False] Audience Appropriateness[True, False] measurable attributes (e.g. Reasoning Depth or Distractor Quality) with explicit ordinal or categorical scales. This structure decomposes complex benchmark properties into observable units with standardized definitions. Each ordinal indicator is defined with explicit level semantics (e.g. 0–3) to ensure consistent interpretation across annotators and evaluator models; detailed scale definitions are provided in Appendix 5. While some Indicators are conceptually related, they are de- signed to capture distinct aspects of benchmark items. For example, Referential Clarity assesses whether entities are lo- cally resolvable, whereas Ambiguity Level captures broader interpretive uncertainty. Similarly, Language Difficulty re- flects lexical and syntactic complexity, while Readability measures ease of comprehension; Factual Accuracy evalu- ates content truthfulness, whereas Label Quality concerns annotation correctness. Indicators were iteratively refined to minimize semantic redundancy, but statistical indepen- dence is not required: correlations reflect the co-occurrence of linguistic, cognitive, and factual demands and are later exploited for dimensionality analysis and benchmark orches- tration (Section 6). 4.1. LLM-as-a-Judge Operationalization We operationalize these Indicators by converting the Cat- alogue into a structured LLM-as-a-judge protocol. Rather than evaluating model performance on a benchmark, our approach uses the LLM to generate descriptive metadata for the benchmark itself. Each indicator from the Catalogue is translated into a precise sub-prompt featuring: (i) the dis- crete rating scale or categorical options defined in Table 2, (i) a requirement for a short natural-language justification to ensure reasoning transparency, and (i) a JSON-formatted output for robust automated parsing. We execute this protocol using three state-of-the-art eval- uator models – GPT-5 (OpenAI, 2025), DeepSeek-V3.1 (DeepSeek-AI et al., 2025) and DeepSeek-R1 – to annotate every sample across five influential benchmarks: MMLU, ARC, WinoGrande, HellaSwag, and TruthfulQA. The result- ing metadata provides a rich, structured description of each sample’s cognitive requirements, linguistic integrity, and ethical framing. This data is then utilized for both dataset diagnostics (e.g. identifying quality distributions and in- ternal biases) and behavioral attribution (e.g. correlating specific sample features with model successes or failures). 4.2. Meta-Data Generation To enable dynamic benchmark orchestration, each bench- mark item must be annotated with the indicators defined in our Catalogue of Criteria. We generate this structured meta- data using a single, carefully engineered LLM-as-a-judge prompt that encodes the entire hierarchy of dimensions, as- pects, and indicators in one pass (full prompt template in Appendix B). The unified prompt presents all indicator defi- 4 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation nitions, value sets, and output schema together, and instructs the model to return a text in strict JSON format of per-item annotations. For each benchmark sample, the model outputs a complete dictionary of ratings – for example, reasoning depth, reasoning type, language difficulty, factual accuracy, distractor quality, domain, and ethical signals. Because all indicators are requested simultaneously, the model evaluates each item holistically while preserving internal consistency across dimensions. We generate the metadata using the Aleph Alpha Research Eval-Framework (Research, 2025), running three state-of- the-art evaluator models, OpenAI GPT-5, DeepSeek-V3.1 and DeepSeek-R1, with deterministic decoding to ensure reproducibility. The resulting metadata provides, for ev- ery benchmark sample, a rich structured representation of cognitive demands, language quality, task properties, con- textual domain, and ethical or safety considerations. This fine-grained annotation layer underpins both our dataset analyses and the construction of new composite benchmarks in the orchestration framework. 4.3. LLM-as-a-Judge Verification We have labeled100samples for each benchmark by hand to evaluate the human agreement of the LLM-as-a-judge prompt we have been using to generate meta-data. This samples have been picked uniformly across all subjects. We have collected human labels for all26indicators from the Catalogue of Criteria with the same input the LLM-as-a- judge has been given. 4.4. Post-Hoc Evaluation on Orchestrated Benchmark Our Catalogue reveals that benchmarks are not monolithic: individual datasets capture only limited regions of a broader capability space. Benchmark orchestration treats datasets as modular resources from which targeted evaluation subsets can be constructed by specifying indicator constraints (e.g. high Reasoning Depth, strong Distractor Quality, or Safety- Critical content). Subsets may be drawn across datasetsfor example, combining WinoGrande (referential ambiguity), HellaSwag (causal narrative inference), and MMLU (multi- step reasoning) to probe compound evaluation scenarios. We analyze Indicator correlations within ARC, HellaSwag, MMLU, TruthfulQA, and WinoGrande (Figures 15–31) to identify how datasets contribute complementary strengths. This enables criterion-driven evaluation, cross-benchmark integration, and adaptive probing of model weaknesses be- yond static leaderboard scores. 5. Results Our empirical study consists of two stages: (1) Sample-level annotation of five influential benchmarks to generate struc- tured metadata, and (2) dynamic benchmark re-sampling followed by model evaluation on the newly constructed test suites. 5.1. Meta-Data For aggregation and visualization, ordinal indicators are treated as ordered categorical variables and summarized via mean values for descriptive comparison; PCA is per- formed on normalized ordinal encodings following standard practice for exploratory analysis (see Appendix 5 for scale semantics). We audit the publicly released test splits of MMLU (all subjects), ARC (Easy and Challenge), Hel- laSwag, TruthfulQA (MC 1&2), and WinoGrande (wino- grandexl) as hosted on Hugging Face, using the Aleph Alpha Eval-Framework. Each item is annotated with26indi- cators drawn from our Catalogue of Criteria (Tables 5 and 6) via an LLM-as-a-judge protocol. A single unified prompt en- codes all indicator definitions, value ranges, and categories in a strict JSON format and is evaluated by three state- of-the-art models – OpenAI GPT-5, DeepSeek-V3.1 and DeepSeek-R1 – using deterministic decoding (temperature 0) to ensure reproducibility and reliability across cognitive, linguistic, task, contextual, and ethical dimensions. Repre- sentative samples are provided in the Appendix (ARC H.5, HellaSwag I.5, MMLU J.5, TruthfulQA K.5, and Wino- Grande L.5). We summarize the resulting annotations in Figure 1, which reports average scores at the Dimension and Aspect levels across all benchmarks. Table 3 lists the corresponding aver- ages for all individual Indicators. Additional visualizations, including Indicator value distributions and per-benchmark Aspect and Dimension averages, are provided in the Ap- pendix (ARC H, HellaSwag I, MMLU J, TruthfulQA K, and WinoGrande L). 5.2. Dynamic Benchmark Orchestration & Model Evaluation To demonstrate how structured metadata enables targeted re-sampling, we designed example benchmark specifica- tions spanning single- and multi-indicator criteria (Table 4). The single-indicator settings isolate one property at a time to create clean, one-dimensional stress tests. For exam- ple, High Reasoning Depth only selects items requiring extended multi-step reasoning, High Language Difficulty only focuses on questions with highly technical vocabulary and syntax, and Strong Bias & Stereotyping only filters for overtly stereotyped or biased content to probe ethical sensitivity. In contrast, the multi-indicator settings combine several metadata Dimensions to construct richer, more challenging slices. High-stakes reasoning under ambiguity stresses care- ful multi-step reasoning where ambiguous wording interacts 5 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Figure 1. Average indicator values aggregated by dimension (top) and aspect (bottom) across all benchmarks. The figure highlights pronounced internal differences in cognitive demands, linguistic quality, task structure, and ethical signaling between benchmarks that are often treated as interchangeable. These systematic differences motivate sample-level auditing and criterion-driven benchmark orchestration rather than reliance on single aggregate scores. Cognitive & Knowledge DemandsEthics, Safety & FairnessLanguage & Content QualityTask Properties Dimension 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Average Value Average per Dimension (excluding Context) Benchmark arc hellaswag mmlu truthfulqa winogrande Clarity & ReadabilityEthical SignalsKnowledgeReasoningStructureTruthfulness Aspect 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 Average Value Average per Aspect (excluding Domain) Benchmark arc hellaswag mmlu truthfulqa winogrande Table 3. Average indicator scores by benchmark. Numeric cells show mean values for all LLM-as-a-judge models (highest per row in bold); categorical cells show top 3 categories with percentage frequencies. Indicator (range)MMLUTruthfulQAARCHellaSwagWinogrande 1. Cognitive & Knowledge Demands reasoningdepth [0, 1, 2, 3]1.040.570.850.991.07 reasoningtype [causal, temporal, counterfactual, abductive, analogical, symbolic]None (38%); causal (29%); abductive (10%)None (62%); causal (17%); abductive (9%)causal (53%); None (31%); abductive (7%)causal (39%); temporal (32%); None (16%)causal (82%); abductive (12%); temporal (4%) knowledge type [common, specialized, scientific, numerical, cultural, narrative]specialized (47%); scientific (19%); cultural (14%) common (38%); cultural (35%); scientific (11%)scientific (56%); common (38%); specialized (4%) common (59%); cultural (14%); narrative (13%)common (77%); narrative (15%); cultural (7%) factrecall [True, False]0.490.700.500.170.00 narrative understanding [True, False]0.170.040.020.320.59 age level [elementary, secondary, undergraduate, postgraduate]secondary (45%); undergraduate (41%); post- graduate (11%) secondary (78%); elementary (19%); undergrad- uate (3%) secondary (64%); elementary (35%); undergrad- uate (0%) secondary (71%); elementary (27%); undergrad- uate (2%) secondary (53%); elementary (47%) 2. Language & Content Quality languagedifficulty [0, 1, 2, 3]1.120.400.570.710.43 spelling [0, 1, 2]1.971.982.001.841.97 grammar [0, 1, 2]1.981.961.991.411.86 referentialclarity [0, 1, 2, 3]2.892.872.992.272.43 ambiguitylevel [0, 1, 2, 3]0.300.710.080.670.68 readability [0, 1, 2, 3]2.202.782.722.212.71 factual accuracy [correct, dubious, incorrect]correct (65%); Correct (32%); dubious (2%)correct (53%); Correct (27%); dubious (14%)correct (66%); Correct (33%); dubious (0%)correct (59%); Correct (28%); dubious (8%)correct (65%); Correct (32%); dubious (2%) factcheckingrequired [True, False]0.600.750.320.190.03 verifiability [no, partial, yes]yes (80%); partial (17%); no (3%)yes (73%); partial (21%); no (6%)yes (96%); partial (3%); no (0%)yes (46%); partial (30%); no (24%)no (48%); yes (44%); partial (8%) 3. Task Properties answerability [no, partial, yes]yes (99%); partial (1%); no (0%)yes (89%); partial (9%); no (2%)yes (100%); partial (0%); no (0%)yes (96%); partial (4%); no (0%)yes (99%); partial (0%); no (0%) labelquality [correct, dubious, incorrect]correct (64%); Correct (32%); dubious (1%)correct (51%); Correct (30%); dubious (12%)correct (66%); Correct (33%); incorrect (0%)correct (65%); Correct (32%); dubious (2%)correct (65%); Correct (33%); dubious (1%) distractorquality [0, 1, 2, 3]1.961.681.410.821.36 temporalsensitivity [True, False]0.090.230.010.030.00 leakagerisk [low, medium, high]low (66%); medium (33%); high (1%)low (70%); medium (27%); high (3%)low (77%); medium (13%); high (11%)low (89%); medium (11%); high (0%)low (68%); medium (17%); high (14%) 4. Context domain [math, computerscience, physics, ..., other]law (13%); philosophy (11%); psychology (10%) culturalreligious (17%);everyday (15%); popculture (10%) biology (39%); physics (24%); educationexams (15%) everyday (74%); medicine (5%); technol- ogyinternet (3%) everyday (84%); educationexams (11%); psy- chology (1%) 5. Ethics, Safety & Fairness biasstereotyping [0, 1, 2, 3]0.030.140.000.050.07 culturalpoliticalframing [True, False]0.050.050.000.000.01 misinformation bait [0, 1, 2, 3]0.041.150.040.200.04 safetycritical [True, False]0.060.040.010.050.00 audienceappropriate [True, False]1.000.981.000.991.00 with safety-critical content, while narrative commonsense with strong distractors probes story understanding and re- sistance to highly plausible but incorrect options. Together, these specifications illustrate how structured annotations enable criterion-driven re-sampling across diverse bench- marks – from clean, single-dimension slices to complex, multi-faceted stress tests. For the baseline and all re-sampling settings we report re- sults using Llama-3.2-1B by Meta (Llama Team, 2024) (all indicators: F, orchestrated indicators: 4) and SmolLM-1.7B- Instruct by HuggingFaceTB (all indicators: E, orchestrated indicators: D), a compact open-weights LLM chosen for efficient, reproducible evaluation. The baseline reflects the full original splits, while the single- and multi-indicator rows show performance on the filtered subsets. 5.3. Human Agreement To validate the reliability of the metadata generated by our LLM-as-a-judge protocol, we conducted a human agree- ment study on100randomly selected samples from each benchmark. These samples were chosen uniformly across all subjects to ensure representative coverage. Human an- notators were provided with the same input and definitions for all26Indicators from the Catalogue of Criteria used by the evaluator models. As illustrated in Figure 2, we observe strong alignment between human experts and the evaluator models (GPT-5 and DeepSeek-V3.1). Key findings include: Correlation Strengths: Indicators such as Reasoning Depth, Fact Recall, and Language Diffi- culty showed high Spearman human-model correlation coef- ficients (ranging from0.74to0.83), suggesting that LLMs are highly capable of identifying these objective structural properties. Subjective Variance: More subjective indi- cators, such as readability and ambiguity level, exhibited slightly lower but still significant agreement (Spearman ρ≈ 0.54to0.62). This variance likely reflects the inherent difficulty in standardizing judgments of linguistic nuance even among humans. High-Stakes Accuracy: For binary indicators like Safety Critical and Factual Accuracy, the models achieved near-perfect alignment with human labels, which is critical for the reliability of our orchestrated safety- 6 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Table 4. Combined specification and evaluation results for baseline and re-sampled subsets using Llama 3.2 1B. Each subset is defined by indicator conditions and intended challenge. Counts indicate samples per dataset; Average Accuracy is computed over filtered subsets. WinograndeTruthfulQAHellaSwagARCMMLUAverage CountAverage AccuracyCountAverage AccuracyCountAverage AccuracyCountAverage AccuracyCountAverage AccuracyCountAverage Accuracy All Samples12670.57916340.187100420.47735480.484140420.39661060.425 Single Indicator-Criteria High language difficulty----30.66740.50011660.3603910.509 High reasoning depth320.406120.24360.000590.33125340.2785280.252 Strong bias stereotyping110.727520.436540.407--640.525450.524 Multi Indicator-Criteria Narrative commonsense with strong distractors600.45020.500290.24110.0002810.341740.306 High-stakes reasoning under ambiguity--830.2045620.50790.2146720.3913310.329 reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_type knowledge_type age_level factual_accuracy verifiability answerability label_quality leakage_risk domain GPT-5 DeepSeek V3 DeepSeek R1 0.270.66-0.060.420.130.27-0.06-0.060.230.520.120.430.270.170.430.350.35-0.230.430.310.190.120.240.150.110.40 0.260.570.030.480.040.30-0.00-0.070.380.540.260.400.310.120.450.390.12-0.210.420.590.260.260.190.10-0.030.37 0.240.600.010.560.240.300.04-0.070.420.54-0.000.360.300.190.340.300.31-0.170.420.430.24-0.010.150.100.050.42 Human-Model Alignment (All Benchmarks) 1.0 0.5 0.0 0.5 1.0 Spearman Correlation Figure 2. Human-model agreement for sample-level indicator annotations across all benchmarks. Spearman correlations show strong alignment between human annotations and evaluator models, particularly for structurally grounded indicators (e.g. Reasoning Depth, Factual Accuracy), supporting the use of LLM-as-a-judge for scalable benchmark auditing. sensitive subsets. These results demonstrate that the LLM- as-a-judge framework is a robust proxy for manual dataset auditing, enabling the scalable generation of the metadata required for dynamic orchestration. 6. Discussion The Indicator counts and distributions reveal pronounced differences in the internal makeup of the benchmarks consid- ered in this study. By decomposing these datasets along our proposed five Dimensions, we can interpret model perfor- mance through the specific cognitive, linguistic, and ethical demands of the samples. 6.1. Benchmark Profiles and Latent Demands Our meta-evaluation framework exposes distinct character- istic profiles for each influential benchmark: MMLU: High Intensity and Specialized Knowledge MMLU concentrates the most challenging material, con- taining over 2.5k items at Reasoning Depth≥ 2and more than 1.1k items at high language difficulty levels. It serves as a primary test of academic and professional expertise rather than pure commonsense. ARC: Scientific RecallDespite its focus on science, ARC rarely moves beyond minimal Reasoning Depth. It remains a broad but relatively shallow test of scientific knowledge and world facts. WinoGrande and HellaSwag: Adversarial Heuristics WinoGrande is dominated by shallow causal reasoning (depth 1 in 97% of items). In contrast, HellaSwag blends everyday scenarios with high rates of ambiguity and gram- mar variability, reflecting its adversarial design intended to challenge shallow model heuristics. TruthfulQA: Ethical and Safety SignalingTruthfulQA contains the strongest ethical signals in our study, including over 1k moderate and 64 high misinformation-bait items, alongside the majority of safety-critical cases. 6.2. The Performance-Complexity Gap The results in Table 4 confirm that what appears as a sin- gle benchmark score in standard practice actually reflects highly divergent mixtures of risk and demand. We observe that model performance often drops sharply on orchestrated subsets that isolate high-reasoning, high-difficulty, or safety- critical slices. This suggests that current leaderboards may overstate model reliability by aggregating results across ”easy” samples that do not require multi-step inference or ethical sensitivity. 6.3. PCA and the Evaluation Space Our Principal Component Analysis (PCA) of the Indicator space (Figure 3 and 4) provides a map of the latent dimen- sions of current benchmarks. These latent axes correspond closely to the Indicator dimensions used for orchestration, providing empirical justification for constructing benchmark 7 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Figure 3. Principal Component Analysis (PCA) of the sample-level indicator space across all benchmarks. The first principal com- ponents separate samples dominated by specialized knowledge and fact recall from those emphasizing narrative understanding and commonsense reasoning. This structure reveals latent evalua- tion dimensions that cut across benchmark boundaries, motivating dynamic orchestration of benchmark subsets along interpretable criteria rather than fixed dataset partitions. Figure 4. Latent structure of benchmark samples projected into the indicator space. Samples from different benchmarks occupy distinct but overlapping regions, illustrating that no single bench- mark isolates a unique capability. This overlap further supports cross-benchmark orchestration to construct targeted evaluation slices that combine complementary challenges. subsets along combined criteria such as Reasoning Depth, Ambiguity, and Safety-Critical Relevance. The primary axis of variance separates datasets heavy on specialized knowledge and fact recall (MMLU, ARC) from those centered on narrative understanding and common- sense (HellaSwag, WinoGrande). The clustering of indi- cators such as Grammar and Fact Checking Requirement suggests that linguistic precision and factual accuracy are often inextricably linked in current evaluation data. This richer view encourages a shift toward dynamic benchmarks that deliberately sample challenges most relevant to specific application domains. These latent axes provide the empir- ical basis for constructing orchestrated benchmark slices that combine multiple Indicator constraints, as explored in Section 4.4. 7. Limitations We do not claim that the proposed Indicators exhaustively characterize all forms of model difficulty, but rather that they expose dimensions that are systematically overlooked by ag- gregate benchmark evaluation. While our meta-evaluation framework provides a necessary lens into the granular com- position of benchmarks, it is subject to several critical lim- itations that warrant consideration. A primary concern is the potential for evaluator model bias, as the reliance on high-capacity models like GPT-5 or DeepSeek-V3.1 to gen- erate metadata may lead to ”self-enhancement” biases or a preference for linguistic patterns found in the evaluators’ own training data. This creates a risk of circular depen- dency if the framework is integrated into the training loop; flawed metadata could cause a model to overfit to the judges idiosyncratic preferences rather than developing genuine Reasoning Depth or Ethical Sensitivity. Furthermore, the audit reveals a significant sparsity of high-stakes samples, specifically for Indicators like level-3 reasoning, which may limit the statistical power of orchestrated subsets to distin- guish between top-tier models. The framework also relies on subjective human-model alignment, where the ground truth for dimensions such as Ambiguity or Readability is inherently difficult to standardize across diverse cultural con- texts. Finally, because these annotations represent a static snapshot, factors like Factual Accuracy or Temporal Sensi- tivity may degrade over time as world knowledge evolves, necessitating periodic re-auditing of the datasets. While this work focuses on evaluation, the structured metadata pro- duced by our framework could support future integration of orchestrated benchmarks into training or fine-tuning loops. 8. Conclusion We present a meta-evaluation framework that audits bench- mark datasets at the sample level and supports dynamic orchestration across five hidden Dimensions. Annotating five influential benchmarks using a LLM-as-a-judge and re-sampling targeted subsets shows that LLM performance shifts sharply when evaluation isolates specific cognitive, linguistic, or ethical Indicators. The framework offers fine- grained diagnostics, enables more meaningful model com- parisons, and supports adaptive benchmarks that evolve with model capabilities while guiding safer deployment by surfacing failure modes in high-stakes or bias-sensitive con- texts. 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URL https://aclanthology.org/N18-2003/. Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., Zhang, H., Gonzalez, J. E., and Stoica, I. Judging LLM-as- a-Judge with MT-Bench and Chatbot Arena, Decem- ber 2023. URLhttp://arxiv.org/abs/2306. 05685. arXiv:2306.05685 [cs]. 13 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation A. Catalogue of Criteria Table 5. Part 1: Catalogue of Criteria Dimensions, Aspects and Indicators for sample-level meta-data generation and re-sampling of new benchmarks. DimensionAspectIndicatorValueDescriptionExample/Guideline Cognitive & Knowledge DemandsReasoningReasoning Depth0None; direct recallCapital of France 1Minimal; one simple inferenceIf today is Monday, what day is tomorrow? 2Moderate; several linked stepsJohn > Mary > Alice. Who is youngest? 3 Extended chain; multi-step deriva- tion Multi-step puzzle or proof Reasoning TypecausalCauseeffect relationShe fell because she tripped temporalTime/sequence reasoningIf its 3pm in Paris, what time in NYC? counterfactualHypothetical reasoningIf it had rained, the ground would be wet abductiveBest-explanation inferenceThe ground is wet it probably rained analogicalReasoning by analogyHand is to glove as foot is to sock symbolicFormal/logical inferenceIf A>B and B>C, then A>C KnowledgeKnowledge TypecommonEveryday knowledgeCats have tails specializedProfessional/technicalLegal contract clause scientificNatural/formal sciencesH2O is water numericalQuantitative/mathematical5×6=30 culturalCultural norms/referencesThanksgiving is US holiday narrativeStory/world knowledgeTracking characters in passage Fact RecallTrueFact lookup sufficesCapital of France = Paris FalseReasoning requiredWho is tallest if John > Mary > Alice? Narrative UnderstandingTrueStory/event tracking neededFollowing a story arc FalsePurely factual2+2=4 Age AppropriatenessElementaryBasic knowledgeSun rises in east SecondaryHigh-school levelPythagoras theorem UndergraduateUniversity-levelShakespearean themes PostgraduateAdvanced expertiseMedical licensing exam item Language & Content QualityClarity & ReadabilityLanguage Difficulty0Very simpleDog is an animal 1Moderate complexityThe parliament convened yesterday 2Specialized vocabularyThe mitochondria is the powerhouse of the cell 3Highly technicalEigenvalue decomposition of covariance ma- trices Spelling0Severe errors; unreadableThs is incorect. 1Minor errors; still readableOne or two typos 2Correct spelling throughoutNo misspellings Grammar0Very poor grammar; confusingShe go store. 1Minor issues; understandableHe dont know. 2Grammatically correctStandard grammar Referential Clarity0Very ambiguousHe was upset (unclear referent) 1Somewhat clearSome ambiguity remains 2Mostly clearMinor ambiguity 3Fully clearNo ambiguity Ambiguity Level0UnambiguousOnly one interpretation 1Slight ambiguityContext resolves it 2Moderate ambiguityMultiple plausible answers 3High ambiguitySeveral equally valid answers Readability0Very difficultDisfluent/awkward phrasing 1Somewhat difficultComplex style 2Moderately easyStandard style 3Very easyPlain and fluent TruthfulnessFactual AccuracyCorrectFactually correctVerified Wikipedia statement DubiousUncertain/mixed accuracyMisquoted fact IncorrectClearly wrongParis is in Germany Fact Checking RequirementTrueNeeds external infoCurrent population of Paris FalseSelf-contained2+2=4 VerifiabilityYesFully checkable from sampleExplicit answer in passage PartialSome info missingRequires assumption NoCannot be checkedOpinion question Task PropertiesStructureAnswerabilityYesFully answerable from contextClear span in passage PartialPartially answerableMissing some details NoUnanswerablePassage unrelated Label QualityCorrectGold label unambiguousCorrect answer provided DubiousGold label questionableMultiple valid answers IncorrectGold label wrongAnnotator error MCQ Distractor Quality0ImplausibleObviously wrong option 1WeakEasy to dismiss 2Mostly plausibleMinor flaws 3Strong/fairConvincing distractors Temporal SensitivityTrueTime-dependentCurrent president FalseTimeless2+2=4 Provenance / Leakage RiskLowUnlikely overlap with trainingSynthetic puzzle MediumPossible overlapCommon textbook fact HighLikely overlapWikipedia lead sentence 14 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Table 6. Part 2: Catalogue of Criteria Dimensions, Aspects and Indicators for sample-level meta-data generation and re-sampling of new benchmarks. DimensionAspectIndicatorValueDescriptionExample/Guideline ContextDomainTopical DomainMathPure / applied mathematicsWhat is the derivative of x 2 ? Computer ScienceProgramming, algorithms, AIWhat is a linked list? PhysicsPhysical sciencesWhat is Newtons 2nd law? ChemistryChemical sciencesWhat is H2O? BiologyLife sciencesWhat does DNA stand for? MedicineHealth, clinical, biomedicalWhat is hypertension? EngineeringApplied engineering tasksWhat is Ohms law? MathPure / applied mathematicsWhat is the derivative of x 2 ? LiteratureLiterary studiesWho wrote Hamlet? HistoryHistorical knowledgeWho was the first US president? PhilosophyLogic, ethics, metaphysicsExplain utilitarianism Arts / MusicFine arts, performanceWho painted the Mona Lisa? EconomicsEconomic theory / practiceDefine opportunity cost PsychologyPsychological conceptsWhat is Pavlovian conditioning? SociologySocial structures, cultureWhat is social stratification? Political ScienceGovernance, international relationsWhat is separation of powers? LawLegal systems, contractsWhat is habeas corpus? Business / FinanceMarkets, accounting, commerceWhat is ROI? Education / ExamsTest-style questionsSAT, GRE, high-school exam items Technology / InternetDigital culture, ITWhat is HTTP? Everyday KnowledgeCommonsense, daily lifeHow to boil water? Pop Culture / EntertainmentMovies, TV, sports, celebritiesWho plays Iron Man? Cultural / Religious KnowledgeReligion, traditions, holidaysWhat is Ramadan? News / Current EventsRecent / time-sensitive infoWho is the current UN Secretary-General? General TriviaMixed / broad questionsPub quiz style facts OtherNot fitting aboveNiche or unusual topics Ethics, Safety & FairnessEthical SignalsBias & Stereotyping0NoneNeutral phrasing 1WeakSlight stereotype implied 2ModerateClear stereotype presence 3StrongExplicit stereotype Cultural / Political FramingTrueRequires stanceUS-specific law reference FalseNeutralGeneral knowledge Misinformation Bait0NoneNo misconception risk 1WeakSlightly misleading phrasing 2ModerateCommon misconception implied 3HighDirect false belief framing Safety-Critical RelevanceTrueHigh-stakes errorsMedical/legal advice FalseLow-stakesTrivia question Audience AppropriatenessTrueAppropriate for audienceNeutral tone FalseOffensive/inappropriateContains slur 15 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation B. Prompt Template Meta-Data Generation Prompt Template You are an expert dataset auditor. Annotate EACH input item with the indicators below. Return STRICT JSON only (a JSON array of objects). Do NOT add commentary or extra keys. If uncertain, pick the closest anchor and include a brief note (30 words) in ‘notes‘. ### INDICATORS (grouped by numbered dimensions with full scale descriptions) 1. Cognitive & Knowledge Demands - reasoning_depth: 0 = None (direct recall, no inference) 1 = Minimal (one simple inference) 2 = Moderate (several linked steps) 3 = Extended (multi-step chain or puzzle) - reasoning_type: causal, temporal, counterfactual, abductive, analogical, symbolic // multi-select - knowledge_type: common, specialized, scientific, numerical, cultural, narrative // multi-select - fact_recall: true = fact lookup suffices; false = reasoning required - narrative_understanding: true = requires tracking story/events; false = purely factual - age_level: elementary | secondary | undergraduate | postgraduate 2. Language & Content Quality - language_difficulty: 0 = Very simple (basic words, short sentences) 1 = Moderate (mixed structure, some technical terms) 2 = Complex (specialized vocabulary, long sentences) 3 = Highly technical (field-specific terminology) - spelling: 0 = Severe errors; unreadable 1 = Minor errors; still readable 2 = Correct spelling throughout - grammar: 0 = Very poor grammar; confusing 1 = Minor issues; understandable 2 = Grammatically correct - referential_clarity: 0 = Very ambiguous (unclear referent) 1 = Somewhat clear (ambiguity remains) 2 = Mostly clear (minor ambiguity) 3 = Fully clear (no ambiguity) - ambiguity_level: 0 = Unambiguous (one interpretation) 1 = Slight ambiguity (context resolves it) 2 = Moderate ambiguity (multiple plausible answers) 3 = High ambiguity (several equally valid answers) - readability: 0 = Very difficult (disfluent/awkward phrasing) 1 = Somewhat difficult (complex style) 2 = Moderately easy (standard style) 3 = Very easy (plain and fluent) - factual_accuracy: C = Correct, D = Dubious, I = Incorrect - fact_checking_required: true = needs external info; false = self-contained - verifiability: "no" | "partial" | "yes" 3. Task Properties - answerability: "no" | "partial" | "yes" - label_quality: C = Correct, D = Dubious, I = Incorrect - distractor_quality: 0 = Implausible (obviously wrong option) 1 = Weak (easy to dismiss) 2 = Mostly plausible (minor flaws) 3 = Strong (fair, convincing) - temporal_sensitivity: true = time-dependent; false = timeless - leakage_risk: "low" = unlikely overlap, "medium" = possible overlap, "high" = likely overlap 4. Context - domain: math, computer_science, physics, chemistry, biology, medicine, engineering, literature, history, philosophy, arts_music, economics, psychology, sociology, political_science, law, business_finance, education_exams, technology_internet, everyday, pop_culture, cultural_religious, news, trivia, other // single select 5. Ethics, Safety & Fairness - bias_stereotyping: 0 = None 1 = Weak (slight stereotype implied) 2 = Moderate (clear stereotype presence) 3 = Strong (explicit stereotype) - cultural_political_framing: true = requires stance; false = neutral - misinformation_bait: 0 = None 1 = Weak (slightly misleading phrasing) 2 = Moderate (common misconception implied) 3 = High (direct false belief framing) - safety_critical: true = high-stakes (medical/legal/safety); false = low-stakes (trivia) - audience_appropriate: true = appropriate; false = offensive/inappropriate --- ### STRICT OUTPUT SCHEMA "indicators": "reasoning_depth": 0|1|2|3, "reasoning_type": ["causal","temporal","counterfactual","abductive","analogical","symbolic"], "knowledge_type": ["common","specialized","scientific","numerical","cultural","narrative"], "fact_recall": true|false, "narrative_understanding": true|false, "age_level": "elementary"|"secondary"|"undergraduate"|"postgraduate", "language_difficulty": 0|1|2|3, "spelling": 0|1|2, "grammar": 0|1|2, "referential_clarity": 0|1|2|3, "ambiguity_level": 0|1|2|3, "readability": 0|1|2|3, "factual_accuracy": "C"|"D"|"I", "fact_checking_required": true|false, "verifiability": "no"|"partial"|"yes", "answerability": "no"|"partial"|"yes", "label_quality": "C"|"D"|"I", "distractor_quality": 0|1|2|3, "temporal_sensitivity": true|false, "leakage_risk": "low"|"medium"|"high", "domain": "math"|"computer_science"|"physics"|"chemistry"|"biology"|"medicine"|"engineering"| "literature"|"history"|"philosophy"|"arts_music"|"economics"|"psychology"|"sociology"| "political_science"|"law"|"business_finance"|"education_exams"|"technology_internet"| "everyday"|"pop_culture"|"cultural_religious"|"news"|"trivia"|"other", "bias_stereotyping": 0|1|2|3, "cultural_political_framing": true|false, "misinformation_bait": 0|1|2|3, "safety_critical": true|false, "audience_appropriate": true|false , "notes": string ### INSTRUCTIONS 1) Use ONLY the allowed codes and value sets above. 2) For multi-select fields (‘reasoning_type‘, ‘knowledge_type‘) return an array. Use [] if none apply. 3) For domain, pick the closest match from the controlled vocabulary. 4) Treat ordered categories as ordinal for interpretation but ALWAYS output the exact code (e.g. "partial", not 1). 5) If any required field is not fully inferable, choose the closest anchor and explain in ‘notes‘. 6) Do NOT include probabilities, confidence scores, or extra fields. 7) Return ONLY a JSON array matching the schema, one object per input item, in the same order as INPUT. ### INPUT INPUT_JSON ### OUTPUT 16 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation C. All Indicator Counts (GPT-5) Table 7. All indicator configurations: sample counts. Part 1 IndicatorValueWinograndeTruthfulQAHellaSwagARCMMLUTotal agelevelelementary250348194316355404716 agelevelpostgraduate-2--21902192 agelevelsecondary1017126780271913635418578 agelevelundergraduate-1772-49585047 ambiguitylevel036142648473052970618392 ambiguitylevel1617355351844532968231 ambiguity level22856801632499903636 ambiguitylevel3417345250274 answerabilityno24799562215 answerabilitypartial1528378152001284 answerabilityyes12501304916235381378029034 audienceappropriatefalse1870-483 audienceappropriatetrue12661626997235481403830450 biasstereotyping012221483970435481372329680 biasstereotyping13499284-255672 biasstereotyping2113845-60154 biasstereotyping3-149-427 culturalpoliticalframingfalse126715841003035481391730346 culturalpoliticalframingtrue-5012-125187 distractorquality07565287019623091 distractorquality15373185960106114889364 distractorquality252782911652141884213504 distractor quality31284224732736504574 domainartsmusic113195-42251 domainbiology-701211056441831 domainbusinessfinance3211986764992 domainchemistry--4283323610 domaincomputerscience-521389397 domainculturalreligious112463-219407 domaineconomics-4522788837 domaineducationexams4022424712126342519 domainengineering-11412150177 domaineveryday851296791062749193 domainhistory-94-139601067 domainlaw-110122-17972029 domainliterature-19511742 domainmath-16513856890 domainmedicine11144271914201981 domainnews-195-125 domainother-61414118179 domainphilosophy-82-15711581 domainphysics-2626716661365 domainpoliticalscience-269-516551 domainpopculture4156216-90466 domainpsychology-58242-14721772 domainsociology-324-278314 domaintechnologyinternet426333427394 domaintrivia-32593325662 factcheckingrequiredfalse124044581991766486316513 fact checkingrequiredtrue27118918431782917914020 factrecallfalse126131479361061549016062 factrecalltrue6132021062487855214471 17 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Table 8. All indicator configurations: sample counts. Part 2 IndicatorValueWinograndeTruthfulQAHellaSwagARCMMLUAverage factualaccuracycorrect12331313903335021344428525 factualaccuracydubious33305985414841848 factualaccuracyincorrect116245114160 grammar01-34-237 grammar13141518148556659333 grammar29521483186034931337521163 knowledgetypecommon1267121895502235199216262 knowledge typecultural14496515783329405660 knowledgetypenarrative46118289848804261 knowledgetypenumerical6100869716621951 knowledgetypescientific6322315330543658313 knowledgetypespecialized-2117041431093511993 labelqualitycorrect12231157957735091322628692 labelqualitydubious26337410194101202 labelqualityincorrect181405520406639 languagedifficulty0922139027412272293310258 languagedifficulty134524472981272994319102 languagedifficulty2--3411321139 languagedifficulty3----3434 leakageriskhigh5461379011484832404 leakagerisklow20481671341488388313525 leakageriskmedium5176812817912967614603 misinformationbait01239358826434131324426518 misinformationbait1141961086564251777 misinformationbait2141016682793712162 misinformationbait3-6410-276 narrativeunderstandingfalse7631629720735451298326127 narrativeunderstandingtrue50452835310594406 readability0--13--13 readability16-229-390625 readability2336907731434574714338 readability3925154420693114790515557 reasoningdepth0-1118901145962659743 reasoningdepth1123550491352030524318147 reasoningdepth2321265924782587 reasoningdepth3----5656 reasoningtypeabductive2959326811909604219 reasoningtypeanalogical511311418931171 reasoningtypecausal11691243072156825518484 reasoningtypecounterfactual-8-24050 reasoningtypesymbolic865128720332205 reasoning typetemporal475234951336214348 referentialclarity035412841 referentialclarity1320793701120890 referentialclarity25141766306277357758 referentialclarity34301374336235191315921844 safetycriticalfalse12671551948035391337029207 safetycriticaltrue-8356296721326 spelling0--10--10 spelling139412802156193516 spelling212281593723035331342327007 temporalsensitivityfalse12671289983335431330329235 temporalsensitivitytrue-34520957391298 verifiabilityno8401943094307824940 verifiabilitypartial102371370525823376773 verifiabilityyes3251069324332601092318820 18 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation D. SmolLM-1.7B-Instruct Performance on Orchestrated Benchmarks (GPT-5) Table 9. Combined specification and evaluation results for baseline and re-sampled subsets using SmolLM-1.7B-Instruct. Each subset is defined by indicator conditions and intended challenge. Counts indicate items per dataset; Average Accuracy is computed over filtered subsets. Indicator ConditionIntended Challenge WinograndeTruthfulQAHellaSwagARCMMLUOverall CountAcc.CountAcc.CountAcc.CountAcc.CountAcc.CountAvg. All Samples12670.5516340.21100420.4735480.53140420.2661060.40 Single Indicator-Criteria High language difficulty languagedifficulty∈2,3 Handling specialized vocabulary and syntax. 30.3340.3311660.233910.30 High Reasoning Depth reasoningdepth∈2,3 Pure multi-hop reasoning.320.41120.3360.33590.5925340.265280.39 Strong Bias Stereotyping biasstereotyping∈2,3 Sensitivity to overt social bias.110.46520.09540.39640.10450.26 Multi Indicator-Criteria Narrative + Strong Distrac- tors narrative=T,distractor=3, bias≤1 Story-based reasoning requiring event tracking with highly plausible distrac- tors. 600.4820.00290.2111.002810.20740.38 High-Stakes Ambiguity depth≥2,ambiguity≥2, safety=T Multi-step reasoning in safety-critical settings where wording is ambiguous. 830.285620.5390.786720.223310.45 19 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation E. SmolLM-1.7B-Instruct Performance on All Indicators (GPT-5) Table 10. All indicator configurations: Average accuracy. IndicatorValueWinograndeTruthfulQAHellaSwagARCMMLUAverage agelevelelementary0.5840.1770.4880.5310.2480.405 agelevelpostgraduate-0.500--0.2330.367 agelevelsecondary0.5350.2170.4640.5300.2590.401 agelevelundergraduate-0.1740.514-0.2710.320 ambiguitylevel00.5430.2050.5040.5260.2630.408 ambiguity level10.5480.2250.4610.5580.2540.409 ambiguitylevel20.5440.2140.3840.4790.2390.372 ambiguity level30.2500.1670.3781.0000.2240.404 answerabilityno0.5000.2110.3030.8000.2750.418 answerabilitypartial0.3330.1830.3620.3330.2300.289 answerabilityyes0.5470.2130.4800.5290.2590.406 audienceappropriatefalse-0.2000.386--0.293 audienceappropriatetrue0.5450.2090.4700.5290.2590.402 biasstereotyping00.5440.2140.4700.5290.2600.404 biasstereotyping10.5880.1910.447-0.2060.358 biasstereotyping20.4550.1200.378-0.1090.265 biasstereotyping3--0.444--0.444 culturalpoliticalframingfalse0.5450.2100.4690.5290.2590.402 culturalpoliticalframingtrue-0.1780.417-0.2210.272 distractorquality00.4800.1350.5350.4430.3400.387 distractorquality10.5770.1820.4560.5350.2580.402 distractorquality20.5160.2230.3830.5290.2500.380 distractorquality30.5620.2100.2130.5190.2760.356 domainartsmusic1.0000.1630.482-0.2800.481 domainbiology-0.1950.7500.5360.2920.443 domainbusinessfinance0.6670.2450.5400.7500.2440.489 domainchemistry--0.7500.4840.2480.494 domaincomputerscience-0.200--0.2820.241 domainculturalreligious-0.1850.492-0.3530.344 domaineconomics-0.1711.0001.0000.2610.608 domaineducationexams0.5620.0860.5140.5190.2220.381 domainengineering--0.5000.3330.2690.367 domaineveryday0.5370.1930.4580.5200.2380.389 domainhistory-0.211-0.3470.2420.267 domainlaw-0.2460.566-0.2660.359 domainliterature-0.1850.4001.0000.4070.498 domainmath-0.2500.6000.6590.2660.444 domainmedicine1.0000.1940.5670.5340.2330.506 domainnews-0.150---0.150 domainother-0.5000.4290.5340.1740.409 domainphilosophy--0.500-0.2590.379 domainphysics-0.326-0.5640.2460.379 domainpoliticalscience-0.0770.556-0.2910.308 domainpopculture0.2500.2260.375-0.1750.257 domainpsychology-0.2300.541-0.2690.347 domainsociology-0.1680.750-0.2970.405 domaintechnologyinternet0.5000.3080.4740.1670.1990.330 domaintrivia-0.2300.5560.2500.3260.340 factcheckingrequiredfalse0.5450.1740.4750.5420.2550.398 factcheckingrequiredtrue0.5190.2220.4430.5170.2610.392 factrecallfalse0.5440.1900.4570.5450.2510.397 factrecalltrue0.6670.2130.5150.5230.2660.437 20 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Table 11. All indicator configurations: Average accuracy for SmolLM-1.7B-Instruct. IndicatorValueWinograndeTruthfulQAHellaSwagARCMMLUAverage factualaccuracycorrect0.5450.2180.4720.5300.2600.405 factualaccuracydubious0.5150.1680.4360.5050.2260.370 factualaccuracyincorrect1.0000.1980.5000.3750.2560.466 grammar0--0.529--0.529 grammar10.5190.2060.4720.4960.2590.390 grammar20.5540.2080.4560.5300.2590.401 knowledgetypecommon0.5450.2010.4680.5260.2550.399 knowledgetypecultural0.5490.2120.4470.3830.2470.368 knowledgetypenarrative0.5290.1000.4200.7500.2440.409 knowledge typenumerical0.5000.1570.5470.4540.2570.383 knowledgetypescientific0.5000.2260.5370.5310.2500.409 knowledgetypespecialized-0.2750.5060.4950.2580.383 labelqualitycorrect0.5490.2180.4730.5300.2600.406 labelqualitydubious0.5770.1800.3780.3530.2560.349 labelqualityincorrect0.2220.2010.4360.6270.2080.339 languagedifficulty00.5470.2040.4950.5150.2500.402 languagedifficulty10.5390.2380.4590.5580.2630.412 language difficulty2--0.3330.3330.2290.299 languagedifficulty3----0.1220.122 leakageriskhigh0.5660.2070.3670.5190.2710.386 leakagerisklow0.5150.2090.4790.5420.2610.401 leakageriskmedium0.5340.2080.4460.5210.2550.393 misinformationbait00.5440.1950.4590.5300.2600.397 misinformationbait10.5710.2490.5330.5420.2260.424 misinformationbait20.5710.2120.4910.5000.2500.405 misinformationbait3-0.1090.500--0.304 narrativeunderstandingfalse0.5610.2090.4900.5290.2590.410 narrativeunderstandingtrue0.520-0.4160.6670.2360.460 readability0--0.462--0.462 readability10.167-0.528-0.2250.307 readability20.5420.1890.4620.5520.2500.399 readability30.5480.2100.4870.5260.2760.409 reasoningdepth0-0.2060.5180.5150.2890.382 reasoningdepth10.5480.2120.4640.5400.2490.403 reasoningdepth20.4060.3330.3330.5920.2600.385 reasoningdepth3----0.1890.189 reasoningtypeabductive0.5930.1750.4670.4800.2630.396 reasoningtypeanalogical0.800-0.5500.5560.2580.541 reasoningtypecausal0.5350.1610.4630.5270.2400.385 reasoningtypecounterfactual-0.167-0.5000.1190.262 reasoning typesymbolic0.7500.2180.5000.5560.2540.456 reasoning typetemporal0.5740.2470.4300.5770.2320.412 referentialclarity0-0.1670.2501.0000.2400.414 referential clarity10.5470.1920.4971.0000.2730.502 referentialclarity20.5470.1910.4640.4460.2710.384 referentialclarity30.5440.2130.4760.5300.2570.404 safetycriticalfalse0.5450.2050.4650.5290.2610.401 safetycriticaltrue-0.2840.5280.7780.2170.452 spelling0--0.700--0.700 spelling10.5380.2200.4580.5000.2960.403 spelling20.5450.2080.4730.5290.2590.403 temporalsensitivityfalse0.5450.2060.4700.5290.2610.402 temporalsensitivitytrue-0.2170.3970.5830.2070.351 verifiabilityno0.5260.1810.4430.5900.2560.399 verifiabilitypartial0.6270.1890.4460.6000.2580.424 verifiabilityyes0.5660.2220.5200.5250.2610.419 21 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation 22 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation F. Llama 3.2 1B Performance on All Indicators (GPT-5) Table 12. All indicator configurations (Llama 32B): Average accuracy. Part 1 IndicatorValueWinograndeTruthfulQAHellaSwagARCMMLUAverage agelevelelementary0.6360.2950.4990.5010.3020.447 agelevelpostgraduate----0.2200.220 age levelsecondary0.5650.1600.4720.4700.4160.417 agelevelundergraduate-0.0560.500-0.4120.323 ambiguitylevel00.5900.2810.5130.4970.4210.460 ambiguitylevel10.5900.1420.4740.3980.3510.391 ambiguitylevel20.5440.1610.3820.4330.2940.363 ambiguity level30.5000.2190.3560.5000.4200.399 answerabilityno0.5000.1630.3130.2500.3570.317 answerabilitypartial0.2670.1710.3551.0000.2580.410 answerabilityyes0.5830.1910.4890.4840.4000.429 audienceappropriatefalse1.0000.5000.386-0.6670.638 audienceappropriatetrue0.5790.1860.4780.4840.3960.425 biasstereotyping00.5750.1810.4780.4840.3970.423 biasstereotyping10.6760.1420.468-0.3950.420 biasstereotyping20.7270.4740.400-0.5100.528 biasstereotyping3-0.3330.444-0.7500.509 culturalpoliticalframingfalse0.5790.1880.4770.4840.3960.425 culturalpoliticalframingtrue-0.1730.417-0.4730.354 distractorquality00.6130.2540.5430.4920.3720.455 distractorquality10.6220.2340.4650.5280.4750.465 distractorquality20.5460.1790.3910.4750.3920.397 distractor quality30.5160.1550.1910.4040.3810.329 domainartsmusic1.0000.2380.497-0.1630.475 domainbiology-0.1290.7500.5030.4470.457 domainbusinessfinance0.667-0.5860.6670.2800.550 domainchemistry--0.7500.5040.2310.495 domaincomputerscience-0.250--0.4120.331 domainculturalreligious1.0000.2320.524-0.6110.592 domaineconomics-0.1331.0000.5000.3390.493 domaineducationexams0.5620.0860.5180.4480.4040.404 domainengineering--0.4290.6250.3980.484 domaineveryday0.5880.2360.4680.4210.1810.379 domainhistory-0.160-0.6140.5190.431 domainlaw-0.1360.623-0.4370.399 domainliterature-0.1610.400-0.1880.250 domainmath-0.2500.4000.4720.2190.335 domainmedicine1.0000.1930.5180.4210.4700.520 domainnews-0.372---0.372 domainother-0.5000.2860.5110.6080.476 domainphilosophy-0.0830.500-0.4480.344 domainphysics-0.226-0.5020.3330.354 domainpoliticalscience-0.1900.444-0.3370.324 domainpopculture0.5000.2180.412-0.2000.332 domainpsychology-0.0690.529-0.4500.349 domainsociology-0.1320.750-0.5670.483 domaintechnologyinternet0.2500.1920.4920.5000.4690.381 domaintrivia-0.1760.5561.0000.5790.578 factcheckingrequiredfalse0.5770.2470.4870.4800.3860.436 fact checkingrequiredtrue0.6670.1650.4330.4880.4130.433 factrecallfalse0.5780.2260.4680.3880.2880.390 factrecalltrue0.8330.1780.5100.5240.4150.492 23 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation Table 13. All indicator configurations (Llama32B): Average accuracy. Part 2 IndicatorValueWinograndeTruthfulQAHellaSwagARCMMLUAverage factualaccuracycorrect0.5770.1970.4800.4860.4010.428 factualaccuracydubious0.6360.1580.4550.4660.4130.425 factualaccuracyincorrect1.0000.1330.500-0.3330.492 grammar0--0.529-1.0000.765 grammar10.5610.1180.4810.4780.4130.410 grammar20.5860.1940.4600.4840.3960.424 knowledgetypecommon------ knowledgetypecultural------ knowledgetypenarrative------ knowledgetypenumerical------ knowledge typescientific------ knowledgetypespecialized------ labelqualitycorrect0.5850.1920.4820.4850.4060.430 label qualitydubious0.3850.1860.3780.4890.3000.347 label qualityincorrect0.4440.1300.4000.3960.2840.331 languagedifficulty00.5870.1960.5030.4960.4430.445 languagedifficulty10.5590.1360.4670.4640.3880.403 languagedifficulty2--0.6670.5000.3600.509 languagedifficulty3----0.3120.312 leakageriskhigh0.5810.1960.3740.4760.5120.428 leakagerisklow0.5980.2130.4870.4950.4190.443 leakageriskmedium0.5710.1540.4560.4750.3910.409 misinformationbait00.5770.1750.4700.4880.4020.422 misinformationbait10.7140.1550.5210.3750.3060.414 misinformationbait20.6430.1910.4960.4500.3660.429 misinformationbait3-0.3050.300-0.5000.368 narrativeunderstandingfalse0.6030.1870.4950.4840.3960.433 narrative understandingtrue0.5440.3330.431-0.2900.400 readability0--0.385--0.385 readability10.167-0.528-0.3620.353 readability20.5650.1460.4730.4970.3950.415 readability30.5870.1900.4870.4820.3920.428 reasoningdepth0-0.1820.5210.5420.4700.428 reasoningdepth10.5840.1990.4730.4440.3520.410 reasoningdepth20.4060.243-0.3310.2780.315 reasoningdepth3----0.1160.116 reasoningtypeabductive------ reasoningtypeanalogical------ reasoningtypecausal------ reasoning typecounterfactual------ reasoningtypesymbolic------ reasoning typetemporal------ referentialclarity0--0.2501.0000.5170.589 referentialclarity10.5690.1080.511-0.4150.401 referentialclarity20.5890.1610.4690.3680.3840.394 referentialclarity30.5790.1960.4890.4850.3980.429 safetycriticalfalse0.5790.1860.4750.4840.3970.424 safetycriticaltrue-0.2040.5070.2140.3910.329 spelling0--0.700--0.700 spelling10.6150.0710.4690.4500.3200.385 spelling20.5780.1890.4800.4840.3980.426 temporalsensitivityfalse0.5790.2020.4770.4840.3960.428 temporal sensitivitytrue-0.1340.4640.7500.3430.423 verifiabilityno0.5670.1790.4520.4260.3880.402 verifiabilitypartial0.6370.1980.4570.3810.4020.415 verifiabilityyes0.5940.1860.5240.4930.3980.439 24 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation G. Human-Model Agreement G.1. Average Human-Model Agreement reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_type knowledge_type age_level factual_accuracy verifiability answerability label_quality leakage_risk domain GPT-5 DeepSeek V3 DeepSeek R1 0.270.66-0.060.420.130.27-0.06-0.060.230.520.120.430.270.170.430.350.35-0.230.430.310.190.120.240.150.110.40 0.260.570.030.480.040.30-0.00-0.070.380.540.260.400.310.120.450.390.12-0.210.420.590.260.260.190.10-0.030.37 0.240.600.010.560.240.300.04-0.070.420.54-0.000.360.300.190.340.300.31-0.170.420.430.24-0.010.150.100.050.42 Human Alignment: All Benchmarks 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Figure 5. Enter Caption G.2. ARC Human-Model Agreement reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_type knowledge_type age_level factual_accuracy verifiability answerability label_quality leakage_risk domain GPT-5 DeepSeek V3 DeepSeek R1 0.440.370.35-0.01-0.01-0.020.240.270.21-0.06-0.480.140.46-0.040.14-0.060.54 0.470.300.040.53-0.070.490.070.25-0.350.110.430.15-0.080.29 0.390.360.070.44-0.03-0.070.310.180.090.07-0.240.180.41-0.040.15-0.030.62 Human Alignment: ARC 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Figure 6. Enter Caption 25 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation G.3. HellaSwag Human-Model Agreement reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_type knowledge_type age_level factual_accuracy verifiability answerability label_quality leakage_risk domain GPT-5 DeepSeek V3 DeepSeek R1 0.08-0.230.290.080.18-0.01-0.060.11-0.060.21-0.02-0.010.57-0.12-0.000.15-0.110.00-0.180.050.05 -0.06-0.170.10-0.010.210.150.080.19-0.020.16-0.02-0.010.39-0.02-0.110.15-0.010.090.090.03 0.13-0.140.220.260.340.250.150.160.300.270.47-0.010.390.02-0.080.140.10-0.020.050.040.16 Human Alignment: HELLASWAG 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Figure 7. Enter Caption G.4. MMLU Human-Model Agreement reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_type knowledge_type age_level factual_accuracy verifiability answerability label_quality leakage_risk domain GPT-5 DeepSeek V3 DeepSeek R1 0.420.690.200.360.15-0.030.230.150.370.450.020.690.260.180.41-0.440.26-0.08-0.090.09-0.09-0.100.020.71 0.360.560.290.500.100.110.370.560.090.680.390.22-0.040.54-0.190.23-0.02-0.040.150.82 0.260.560.220.380.200.110.040.400.37-0.100.690.130.22-0.060.52-0.300.220.01-0.080.25-0.07-0.110.71 Human Alignment: MMLU 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Figure 8. Enter Caption 26 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation G.5. TruthfulQA Human-Model Agreement reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_type knowledge_type age_level factual_accuracy verifiability answerability label_quality leakage_risk domain GPT-5 DeepSeek V3 DeepSeek R1 0.040.040.20-0.060.13-0.030.39-0.040.170.330.150.100.26-0.150.050.340.160.030.000.120.040.17 0.040.140.050.29-0.03-0.060.130.360.060.170.360.020.170.33-0.05-0.240.170.520.080.12-0.01-0.07-0.020.06 0.090.24-0.060.24-0.040.14-0.030.410.020.110.310.100.08-0.030.26-0.250.030.250.240.00-0.030.140.050.17 Human Alignment: TRUTHFULQA 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Figure 9. Enter Caption G.6. Winogrande Human-Model Agreement reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_type knowledge_type age_level factual_accuracy verifiability answerability label_quality leakage_risk domain GPT-5 DeepSeek V3 DeepSeek R1 0.200.12-0.01-0.020.170.030.39-0.140.41-0.050.050.070.570.010.15-0.11 -0.140.060.07-0.03-0.11-0.22-0.020.480.150.10-0.07-0.120.00 0.260.040.42-0.03-0.03-0.100.34-0.11-0.010.28-0.100.11-0.02-0.02-0.18-0.040.000.01 Human Alignment: WINOGRANDE 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Figure 10. Enter Caption 27 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation G.7. DeepSeek R1 Human-Model Agreement (Confusion Matrix) 0123 Model 0 1 2 3 Human 0.230.580.190.00 0.110.540.340.01 0.000.170.830.00 0.000.001.000.00 reasoning_depth False True Model False True Human 0.930.07 0.370.63 fact_recall False True Model False True Human 0.660.34 0.650.35 narrative_understanding 0123 Model 0 1 2 3 Human 0.970.030.000.00 0.590.370.040.01 0.090.690.230.00 0.170.500.330.00 language_difficulty 012 Model 0 1 2 Human 0.000.001.00 0.000.300.70 0.000.040.96 spelling 012 Model 0 1 2 Human 0.000.710.29 0.000.290.71 0.000.060.94 grammar 0123 Model 0 1 2 3 Human 0.000.000.001.00 0.000.000.150.85 0.000.100.170.73 0.000.040.140.83 referential_clarity 0123 Model 0 1 2 3 Human 0.620.330.050.00 0.700.230.070.00 0.720.280.000.00 1.000.000.000.00 ambiguity_level 0123 Model 0 1 2 3 Human 0.000.500.500.00 0.000.110.680.21 0.010.100.330.57 0.000.040.080.88 readability False True Model False True Human 0.850.15 0.310.69 fact_checking_required 0123 Model 0 1 2 3 Human 0.210.580.180.03 0.070.690.220.02 0.040.510.410.03 0.190.460.310.04 distractor_quality False True Model False True Human 0.980.02 0.740.26 temporal_sensitivity 0123 Model 0 1 2 3 Human 0.920.060.010.00 0.560.330.110.00 0.500.250.250.00 0.330.330.330.00 bias_stereotyping False True Model False True Human 0.990.01 0.910.09 cultural_political_framing 0123 Model 0 1 2 3 Human 0.900.070.030.00 0.650.160.180.01 0.460.150.310.08 0.250.500.250.00 misinformation_bait False True Model False True Human 1.000.00 0.860.14 safety_critical False True Model False True Human 0.250.75 0.010.99 audience_appropriate None abductive analogical causal counterfactual symbolic temporal Model None abductive analogical causal counterfactual symbolic temporal Human 0.000.140.030.540.040.030.22 0.000.220.000.440.000.220.11 0.000.000.001.000.000.000.00 0.000.140.020.570.040.060.17 0.000.000.000.670.000.000.33 0.000.120.000.250.000.620.00 0.000.200.000.500.070.030.20 reasoning_type elementary postgraduate secondary undergraduate Model elementary postgraduate secondary undergraduate Human 0.540.000.460.00 0.030.030.340.59 0.270.000.690.04 0.030.000.620.35 age_level Correct Dubious Incorrect correct dubious incorrect Model Correct Dubious Incorrect correct dubious incorrect Human 0.000.000.000.000.000.00 0.000.000.000.000.000.00 0.000.000.000.000.000.00 0.970.030.000.000.000.00 0.830.150.020.000.000.00 1.000.000.000.000.000.00 factual_accuracy no partial yes Model no partial yes Human 0.250.170.58 0.230.100.66 0.350.020.63 verifiability no partial yes Model no partial yes Human 0.010.040.95 0.010.000.99 0.000.001.00 answerability Correct Dubious correct dubious incorrect Model Correct Dubious correct dubious incorrect Human 0.000.000.000.000.00 0.000.000.000.000.00 0.980.020.000.000.00 0.940.060.000.000.00 1.000.000.000.000.00 label_quality high low medium Model high low medium Human 0.030.800.17 0.000.880.12 0.000.830.17 leakage_risk 0.0 0.2 0.4 0.6 0.8 1.0 Proportion of Human Labels Confusion Matrices: DeepSeek R1 Figure 11. Enter Caption 28 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation G.8. DeepSeek V3 Human-Model Agreement (Confusion Matrix) 0123 Model 0 1 2 3 Human 0.210.720.070.00 0.060.780.150.01 0.000.500.500.00 0.000.200.800.00 reasoning_depth False True Model False True Human 0.910.09 0.370.63 fact_recall False True Model False True Human 0.670.33 0.640.36 narrative_understanding 0123 Model 0 1 2 3 Human 0.100.900.000.00 0.010.860.120.01 0.000.310.630.06 0.000.170.500.33 language_difficulty 012 Model 0 1 2 Human 0.000.001.00 0.000.040.96 0.000.020.98 spelling 012 Model 0 1 2 Human 0.000.570.43 0.000.500.50 0.000.100.90 grammar 0123 Model 0 1 2 3 Human 0.000.000.001.00 0.000.000.150.85 0.000.100.200.71 0.000.050.190.75 referential_clarity 0123 Model 0 1 2 3 Human 0.680.300.020.00 0.730.250.020.00 0.830.170.000.00 1.000.000.000.00 ambiguity_level 0123 Model 0 1 2 3 Human 0.001.000.000.00 0.000.210.710.07 0.000.120.740.14 0.000.040.500.46 readability False True Model False True Human 0.930.07 0.440.56 fact_checking_required 0123 Model 0 1 2 3 Human 0.390.420.120.06 0.250.550.180.01 0.130.520.240.11 0.070.460.410.06 distractor_quality False True Model False True Human 0.980.02 0.710.29 temporal_sensitivity 0123 Model 0 1 2 3 Human 0.980.010.010.00 0.720.110.170.00 0.750.000.250.00 0.670.330.000.00 bias_stereotyping False True Model False True Human 0.980.02 0.930.07 cultural_political_framing 0123 Model 0 1 2 3 Human 0.930.010.060.00 0.580.040.310.07 0.380.080.310.23 0.250.000.750.00 misinformation_bait False True Model False True Human 0.990.01 0.640.36 safety_critical False True Model False True Human 0.120.88 0.010.99 audience_appropriate None abductive analogical causal counterfactual symbolic temporal Model None abductive analogical causal counterfactual symbolic temporal Human 0.000.050.050.570.020.020.29 0.000.000.000.600.000.400.00 0.000.000.000.000.000.000.00 0.000.030.000.760.000.070.15 0.000.500.000.500.000.000.00 0.000.000.000.330.000.670.00 0.000.120.000.820.000.000.06 reasoning_type elementary postgraduate secondary undergraduate Model elementary postgraduate secondary undergraduate Human 0.830.000.170.00 0.030.090.310.56 0.340.010.630.03 0.040.030.640.30 age_level correct dubious incorrect Model correct dubious incorrect Human 0.980.020.00 0.830.170.00 1.000.000.00 factual_accuracy no partial yes Model no partial yes Human 0.090.180.73 0.040.090.87 0.030.030.94 verifiability no partial yes Model no partial yes Human 0.010.060.93 0.010.000.99 0.000.001.00 answerability correct dubious incorrect Model correct dubious incorrect Human 0.980.020.00 0.950.040.01 1.000.000.00 label_quality high low medium Model high low medium Human 0.000.910.09 0.001.000.00 0.010.960.03 leakage_risk 0.0 0.2 0.4 0.6 0.8 1.0 Proportion of Human Labels Confusion Matrices: DeepSeek V3 Figure 12. Enter Caption 29 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation G.9. GPT-5 Human-Model Agreement (Confusion Matrix) 0123 Model 0 1 2 3 Human 0.410.570.010.00 0.180.760.060.00 0.170.670.170.00 0.000.400.600.00 reasoning_depth False True Model False True Human 0.840.16 0.190.81 fact_recall False True Model False True Human 0.680.32 0.740.26 narrative_understanding 0123 Model 0 1 2 3 Human 0.820.180.000.00 0.500.480.010.00 0.170.740.090.00 0.170.670.170.00 language_difficulty 012 Model 0 1 2 Human 0.000.001.00 0.000.170.83 0.000.040.96 spelling 012 Model 0 1 2 Human 0.000.860.14 0.000.580.42 0.000.190.81 grammar 0123 Model 0 1 2 3 Human 0.000.000.001.00 0.000.080.150.77 0.000.050.200.76 0.000.070.240.69 referential_clarity 0123 Model 0 1 2 3 Human 0.510.300.170.02 0.570.300.120.02 0.610.170.220.00 1.000.000.000.00 ambiguity_level 0123 Model 0 1 2 3 Human 0.000.500.500.00 0.000.000.540.46 0.000.020.400.58 0.000.000.240.76 readability False True Model False True Human 0.790.21 0.270.73 fact_checking_required 0123 Model 0 1 2 3 Human 0.120.420.390.06 0.130.440.360.08 0.030.280.530.15 0.080.300.480.15 distractor_quality False True Model False True Human 0.990.01 0.730.27 temporal_sensitivity 0123 Model 0 1 2 3 Human 0.970.020.010.00 0.720.170.110.00 0.750.000.250.00 0.670.330.000.00 bias_stereotyping False True Model False True Human 1.000.00 0.970.03 cultural_political_framing 0123 Model 0 1 2 3 Human 0.900.030.070.00 0.540.030.380.05 0.230.080.620.08 0.500.000.250.25 misinformation_bait False True Model False True Human 1.000.00 0.790.21 safety_critical False True Model False True Human 0.120.88 0.001.00 audience_appropriate None abductive analogical causal counterfactual symbolic temporal Model None abductive analogical causal counterfactual symbolic temporal Human 0.000.280.010.370.000.050.28 0.000.170.170.330.000.330.00 0.000.000.000.000.000.000.00 0.000.210.020.530.000.080.17 0.000.000.001.000.000.000.00 0.000.000.000.200.000.800.00 0.000.190.000.520.000.050.24 reasoning_type elementary postgraduate secondary undergraduate Model elementary postgraduate secondary undergraduate Human 0.400.000.600.00 0.030.060.280.62 0.290.000.690.02 0.040.010.640.31 age_level correct dubious incorrect Model correct dubious incorrect Human 0.950.040.01 0.820.170.01 1.000.000.00 factual_accuracy no partial yes Model no partial yes Human 0.330.260.41 0.210.130.66 0.330.070.60 verifiability no partial yes Model no partial yes Human 0.030.130.84 0.040.080.88 0.000.020.98 answerability correct dubious incorrect Model correct dubious incorrect Human 0.970.010.02 0.900.070.03 1.000.000.00 label_quality high low medium Model high low medium Human 0.090.430.49 0.220.400.38 0.110.350.54 leakage_risk 0.0 0.2 0.4 0.6 0.8 1.0 Proportion of Human Labels Confusion Matrices: GPT-5 Figure 13. Enter Caption 30 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation H. ARC H.1. Value Counts: Indicators 0123 reasoning_depth 0 500 1000 1500 2000 2500 reasoning_depth deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0500100015002000 abductive abductive, analogical abductive, causal abductive, symbolic abductive, temporal analogical analogical, abductive analogical, causal analogical, symbolic causal causal, abductive causal, abductive, analogical causal, abductive, temporal causal, analogical causal, counterfactual causal, symbolic causal, temporal causal, temporal, abductive causal, temporal, analogical symbolic temporal temporal, abductive temporal, analogical temporal, causal temporal, symbolic reasoning_type reasoning_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 02505007501000125015001750 common common, cultural common, cultural, narrative common, narrative common, numerical common, numerical, cultural common, numerical, scientific common, scientific common, scientific, cultural common, scientific, cultural, narrative common, scientific, narrative common, scientific, numerical common, specialized common, specialized, scientific cultural, scientific narrative narrative, cultural numerical numerical, common numerical, scientific scientific scientific, common scientific, common, cultural scientific, common, narrative scientific, common, numerical scientific, cultural scientific, narrative scientific, numerical scientific, numerical, common scientific, numerical, specialized scientific, specialized scientific, specialized, numerical specialized specialized, common specialized, cultural specialized, cultural, narrative specialized, numerical specialized, scientific specialized, scientific, cultural specialized, scientific, numerical knowledge_type knowledge_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_recall 0 500 1000 1500 2000 2500 fact_recall deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True narrative_understanding 0 500 1000 1500 2000 2500 3000 3500 narrative_understanding deepseek-chatdeepseek-reasonergpt-5-2025-08-07 elementary secondary undergraduate age_level 0 500 1000 1500 2000 2500 age_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 language_difficulty 0 500 1000 1500 2000 2500 3000 language_difficulty deepseek-chatdeepseek-reasonergpt-5-2025-08-07 12 spelling 0 500 1000 1500 2000 2500 3000 3500 spelling deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 grammar 0 500 1000 1500 2000 2500 3000 3500 grammar deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 referential_clarity 0 500 1000 1500 2000 2500 3000 3500 referential_clarity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 ambiguity_level 0 500 1000 1500 2000 2500 3000 3500 ambiguity_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 123 readability 0 500 1000 1500 2000 2500 3000 readability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect factual_accuracy 0 500 1000 1500 2000 2500 3000 3500 factual_accuracy deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_checking_required 0 500 1000 1500 2000 2500 3000 fact_checking_required deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes verifiability 0 500 1000 1500 2000 2500 3000 3500 verifiability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes answerability 0 500 1000 1500 2000 2500 3000 3500 answerability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect label_quality 0 500 1000 1500 2000 2500 3000 3500 label_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 distractor_quality 0 500 1000 1500 2000 distractor_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True temporal_sensitivity 0 500 1000 1500 2000 2500 3000 3500 temporal_sensitivity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 high low medium leakage_risk 0 500 1000 1500 2000 2500 3000 3500 leakage_risk deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0200400600800100012001400 arts_music biology business_finance chemistry computer_science cultural_religious economics education_exams engineering everyday history literature math medicine news other philosophy physics political_science psychology sociology technology_internet trivia domain domain deepseek-chatdeepseek-reasonergpt-5-2025-08-07 01 bias_stereotyping 0 500 1000 1500 2000 2500 3000 3500 bias_stereotyping deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False cultural_political_framing 0 500 1000 1500 2000 2500 3000 3500 cultural_political_framing deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 misinformation_bait 0 500 1000 1500 2000 2500 3000 3500 misinformation_bait deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True safety_critical 0 500 1000 1500 2000 2500 3000 3500 safety_critical deepseek-chatdeepseek-reasonergpt-5-2025-08-07 True audience_appropriate 0 500 1000 1500 2000 2500 3000 3500 audience_appropriate deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Figure 14. Enter Caption 31 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation H.2. Pearson Correlation: Indicators reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate Indicator Pearson Correlation 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Pearson Correlation Figure 15. Heatmap of pairwise Pearson correlations between indicators for ARC. Rows and columns represent individual indicators; cell color encodes the strength and direction of their linear relationship (red = strong positive, blue = strong negative, white = no correlation). Values are computed across all samples and models for the given benchmark. 32 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation H.3. Average Counts: Aspects Clarity & Readability Ethical Signals Knowledge Reasoning Structure Truthfulness Aspect 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 Average Value Average per Aspect Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 16. Enter Caption H.4. Average Counts: Dimensions Cognitive & Knowledge Demands Ethics, Safety & Fairness Language & Content Quality Task Properties Dimension 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Average Value Average per Dimension Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 17. Enter Caption 33 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation H.5. Sample Meta-Data: id 0 Sample Meta-Data: id 0 "id": 0, "subject": "ARC-Easy", "task": "Question: Which is the function of the gallbladder? ", "options": [ " store bile", " produce bile", " store digestive enzymes", " produce digestive enzymes" ], "ground_truth": " store bile", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 0, "reasoning_type": [], "knowledge_type": [ "scientific" ], "fact_recall": true, "narrative_understanding": false, "age_level": "secondary", "language_difficulty": 0, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": true, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "high", "domain": "biology", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Widely-known biology fact; ARC-Easy likely seen in pretraining, hence high leakage risk." , "deepseek-chat": "indicators": "reasoning_depth": 0, "reasoning_type": [], "knowledge_type": [ "specialized", "scientific" ], "fact_recall": true, "narrative_understanding": false, "age_level": "secondary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": true, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "biology", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" 34 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation H.6. Sample Meta-Data: id 1 Sample Meta-Data: id 1 "id": 1, "subject": "ARC-Easy", "task": "Question: Which type of adaptation allows an animal to deceive its predator? ", "options": [ " large size", " protective coloration", " scent glands", " leathery skin" ], "ground_truth": " protective coloration", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [ "causal" ], "knowledge_type": [ "common", "scientific" ], "fact_recall": true, "narrative_understanding": false, "age_level": "elementary", "language_difficulty": 0, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "high", "domain": "biology", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" , "deepseek-chat": "indicators": "reasoning_depth": 1, "reasoning_type": [ "causal" ], "knowledge_type": [ "scientific" ], "fact_recall": false, "narrative_understanding": false, "age_level": "elementary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 1, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "biology", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" 35 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation H.7. Sample Meta-Data: id 2 Sample Meta-Data: id 2 "id": 2, "subject": "ARC-Easy", "task": "Question: Which three things do animals need from their environment in order to survive? ", "options": [ " soil, water, and food", " soil, light, and water", " air, food, and water", " air, water, and light" ], "ground_truth": " air, food, and water", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 0, "reasoning_type": [], "knowledge_type": [ "common", "scientific" ], "fact_recall": true, "narrative_understanding": false, "age_level": "elementary", "language_difficulty": 0, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": true, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "high", "domain": "education_exams", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Elementary science MCQ; direct recall that animals need air, water, and food." , "deepseek-chat": "indicators": "reasoning_depth": 0, "reasoning_type": [], "knowledge_type": [ "common", "scientific" ], "fact_recall": true, "narrative_understanding": false, "age_level": "elementary", "language_difficulty": 0, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 1, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "biology", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" 36 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation I. HellaSwag I.1. Value Counts: Indicators 0123 reasoning_depth 0 2000 4000 6000 8000 reasoning_depth deepseek-chatdeepseek-reasonergpt-5-2025-08-07 01000200030004000 abductive abductive, analogical abductive, analogical, symbolic abductive, causal abductive, causal, temporal abductive, symbolic abductive, temporal abductive, temporal, causal analogical analogical, abductive analogical, abductive, temporal analogical, causal analogical, counterfactual analogical, symbolic analogical, temporal causal causal, abductive causal, abductive, analogical causal, abductive, counterfactual causal, abductive, symbolic causal, abductive, temporal causal, analogical causal, analogical, abductive causal, analogical, symbolic causal, counterfactual causal, counterfactual, abductive causal, counterfactual, analogical causal, symbolic causal, temporal causal, temporal, abductive causal, temporal, analogical causal, temporal, counterfactual causal, temporal, counterfactual, abductive causal, temporal, counterfactual, analogical causal, temporal, symbolic counterfactual counterfactual, causal symbolic symbolic, analogical symbolic, causal temporal temporal, abductive temporal, abductive, causal temporal, analogical temporal, causal temporal, causal, abductive temporal, counterfactual reasoning_type reasoning_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 010002000300040005000 common common, cultural common, cultural, everyday common, cultural, narrative common, cultural, narrative, specialized common, cultural, numerical common, cultural, pop_culture common, cultural, specialized common, narrative common, narrative, cultural common, narrative, numerical common, numerical common, numerical, cultural common, numerical, narrative common, scientific common, scientific, cultural common, scientific, narrative common, scientific, numerical common, scientific, specialized common, specialized common, specialized, cultural common, specialized, cultural, narrative common, specialized, narrative common, specialized, numerical common, specialized, numerical, cultural common, specialized, scientific common, specialized, scientific, cultural common, specialized, scientific, cultural, narrative common, specialized, scientific, narrative common, specialized, scientific, numerical common, specialized, scientific, numerical, cultural common, technology_internet cultural cultural, common cultural, common, narrative cultural, common, specialized cultural, narrative cultural, numerical cultural, scientific cultural, specialized cultural, specialized, narrative narrative narrative, common narrative, cultural narrative, specialized numerical numerical, common scientific scientific, common scientific, common, specialized scientific, cultural scientific, numerical scientific, numerical, common scientific, specialized specialized specialized, business_finance specialized, common specialized, cultural specialized, cultural, narrative specialized, cultural, numerical specialized, everyday specialized, narrative specialized, narrative, cultural specialized, numerical specialized, numerical, common specialized, numerical, cultural specialized, numerical, narrative specialized, scientific specialized, scientific, common specialized, scientific, cultural specialized, scientific, narrative specialized, scientific, numerical knowledge_type knowledge_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_recall 0 2000 4000 6000 8000 fact_recall deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True narrative_understanding 0 1000 2000 3000 4000 5000 6000 7000 narrative_understanding deepseek-chatdeepseek-reasonergpt-5-2025-08-07 elementary postgraduate secondary undergraduate age_level 0 1000 2000 3000 4000 5000 6000 7000 8000 age_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 language_difficulty 0 2000 4000 6000 8000 10000 language_difficulty deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 spelling 0 2000 4000 6000 8000 spelling deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 grammar 0 1000 2000 3000 4000 5000 6000 7000 8000 grammar deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 referential_clarity 0 1000 2000 3000 4000 5000 6000 7000 referential_clarity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 ambiguity_level 0 1000 2000 3000 4000 5000 6000 ambiguity_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 readability 0 1000 2000 3000 4000 5000 6000 7000 8000 readability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect factual_accuracy 0 2000 4000 6000 8000 factual_accuracy deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_checking_required 0 1000 2000 3000 4000 5000 6000 7000 8000 fact_checking_required deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes verifiability 0 1000 2000 3000 4000 5000 6000 verifiability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes answerability 0 2000 4000 6000 8000 10000 answerability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect label_quality 0 2000 4000 6000 8000 10000 label_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 distractor_quality 0 1000 2000 3000 4000 5000 6000 7000 8000 distractor_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True temporal_sensitivity 0 2000 4000 6000 8000 10000 temporal_sensitivity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 high low medium leakage_risk 0 2000 4000 6000 8000 10000 leakage_risk deepseek-chatdeepseek-reasonergpt-5-2025-08-07 02000400060008000 arts_music biology business_finance chemistry computer_science cultural_religious economics education_exams engineering everyday history law literature math medicine news other philosophy physics political_science pop_culture psychology sociology technology_internet trivia domain domain deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 bias_stereotyping 0 2000 4000 6000 8000 10000 bias_stereotyping deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True cultural_political_framing 0 2000 4000 6000 8000 10000 cultural_political_framing deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 misinformation_bait 0 2000 4000 6000 8000 misinformation_bait deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True safety_critical 0 2000 4000 6000 8000 10000 safety_critical deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True audience_appropriate 0 2000 4000 6000 8000 10000 audience_appropriate deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Figure 18. Enter Caption 37 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation I.2. Pearson Correlation: Indicators reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate Indicator Pearson Correlation 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Pearson Correlation Figure 19. Heatmap of pairwise Pearson correlations between indicators for HellaSwag. Rows and columns represent individual indicators; cell color encodes the strength and direction of their linear relationship (red = strong positive, blue = strong negative, white = no correlation). Values are computed across all samples and models for the given benchmark. 38 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation I.3. Average Counts: Aspects Clarity & Readability Ethical Signals Knowledge Reasoning Structure Truthfulness Aspect 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Average Value Average per Aspect Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 20. Enter Caption I.4. Average Counts: Dimensions Cognitive & Knowledge Demands Ethics, Safety & Fairness Language & Content Quality Task Properties Dimension 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Average Value Average per Dimension Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 21. Enter Caption 39 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation I.5. Sample Meta-Data: id 0 Sample Meta-Data: id 0 "id": 0, "subject": "no_subject", "task": "Personal Care and Style: How to increase breast size with a bra. Check your bra size. Wearing a " "bra that is too big will not make your breasts look larger. That is why it is important to wear " "the right size bra for you.", "options": [ " You can visit a lingerie shop and have them measure you to help you fit a bra to your size, or " "measure yourself before you shop for a new bra to ensure that you get a good fit. Use a flexible " "tape measure, like one found in a sewing kit.", " This is why it is important to keep your breasts under protection when in the shower and only wear " "bras that are larger than your breast size. If you are not wearing a bra, try wearing something " "that is a little bigger.", " For a girl, a bra with a support strap will be easier for her, because most women are unable to " "pull through bra straps and bras that are too small will not be able to support breasts from " "side-to-side. Many bras have even been created that cover the breast side, and can be sent to other " "women in the world to make them look bigger.", " Choose a color that is flattering to your breast type and specific event, in addition to those " "that make you uncomfortable. Look for sports bras made from natural material, such as spandex or " "lycra, as this is a more breathable bra." ], "ground_truth": " You can visit a lingerie shop and have them measure you to help you fit a bra to your " "size, or measure yourself before you shop for a new bra to ensure that you get a good " "fit. Use a flexible tape measure, like one found in a sewing kit.", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [], "knowledge_type": [ "common" ], "fact_recall": false, "narrative_understanding": false, "age_level": "secondary", "language_difficulty": 1, "spelling": 2, "grammar": 1, "referential_clarity": 3, "ambiguity_level": 0, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "partial", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 1, "temporal_sensitivity": false, "leakage_risk": "medium", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 2, "safety_critical": false, "audience_appropriate": true , "notes": "Minimal inference from prompt to correct option; some distractors contain " "incorrect/misleading advice." , "deepseek-chat": "indicators": "reasoning_depth": 0, "reasoning_type": [], "knowledge_type": [ "common" ], "fact_recall": true, "narrative_understanding": false, "age_level": "secondary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 0, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" 40 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation I.6. Sample Meta-Data: id 1 Sample Meta-Data: id 1 "id": 1, "subject": "no_subject", "task": "Washing face: A girl stands in front of a bathroom mirror and vigorously rubs " "her face. The girl turns on the faucet. The girl", "options": [ " spits toothpaste into the sink.", " then splashes water on her face several times.", " runs water over her face.", " dries her face off and shaves her face with the razor." ], "ground_truth": " then splashes water on her face several times.", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [ "causal", "temporal" ], "knowledge_type": [ "common", "narrative" ], "fact_recall": false, "narrative_understanding": true, "age_level": "elementary", "language_difficulty": 0, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 2, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "no", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "medium", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Slight ambiguity between 2018splashes water 2019 and 2018runs " "water over her face 2019; both plausible next steps." , "deepseek-chat": "indicators": "reasoning_depth": 1, "reasoning_type": [ "temporal", "causal" ], "knowledge_type": [ "common", "everyday" ], "fact_recall": false, "narrative_understanding": true, "age_level": "elementary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" 41 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation I.7. Sample Meta-Data: id 2 Sample Meta-Data: id 2 "id": 2, "subject": "no_subject", "task": "Home and Garden: How to paint basement stairs. Remove any carpet or overlaid " "material from your basement stairs. Remove staples left from the carpet " "installation with pliers. Look over all areas of the stairs to find holes and " "deep scratches.", "options": [ " Get rid of any floating debris and knock out any plumbing fixtures, doors or " "fittings. Also be sure to remove any railings, cabinets, or sections attached to " "the basement above ground.", " Pound on the stripped carpet with a hammer. In most cases, you’l encounter " "gentle taps caused by hammering along the floor.", " Use putty or wood filler and a putty knife to fill in holes. If you have a " "cement staircase, you will want to fill holes with epoxy.", " Remove any tread tiles or other fixtures that are covered. Keep the stairs cool " "so that water and moisture can flow freely in the stairs and help them to dry." ], "ground_truth": " Use putty or wood filler and a putty knife to fill in holes. If you " "have a cement staircase, you will want to fill holes with epoxy.", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [ "temporal" ], "knowledge_type": [ "common" ], "fact_recall": false, "narrative_understanding": false, "age_level": "secondary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 2, "ambiguity_level": 1, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 0, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Implied next step after inspecting for holes/scratches; choose " "filler/epoxy option. Slightly vague phrasing." , "deepseek-chat": "indicators": "reasoning_depth": 1, "reasoning_type": [ "causal" ], "knowledge_type": [ "common", "specialized" ], "fact_recall": false, "narrative_understanding": false, "age_level": "secondary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 2, "ambiguity_level": 0, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 1, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Distractors are weak and easy to dismiss as incorrect." 42 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation J. MMLU J.1. Value Counts: Indicators 0123 reasoning_depth 0 1000 2000 3000 4000 5000 6000 7000 reasoning_depth deepseek-chatdeepseek-reasonergpt-5-2025-08-07 02000400060008000 abductive abductive, analogical abductive, analogical, symbolic abductive, causal abductive, causal, temporal abductive, counterfactual abductive, symbolic abductive, temporal abductive, temporal, causal analogical analogical, abductive analogical, causal analogical, counterfactual analogical, symbolic analogical, temporal analogical, temporal, abductive causal causal, abductive causal, abductive, analogical causal, abductive, analogical, symbolic causal, abductive, symbolic causal, abductive, temporal causal, analogical causal, analogical, abductive causal, analogical, symbolic causal, counterfactual causal, counterfactual, abductive causal, counterfactual, abductive, symbolic causal, counterfactual, analogical causal, counterfactual, analogical, abductive causal, counterfactual, symbolic causal, counterfactual, temporal causal, symbolic causal, symbolic, abductive causal, symbolic, temporal causal, temporal causal, temporal, abductive causal, temporal, abductive, symbolic causal, temporal, analogical causal, temporal, counterfactual causal, temporal, counterfactual, abductive causal, temporal, counterfactual, analogical causal, temporal, symbolic counterfactual counterfactual, abductive counterfactual, abductive, symbolic counterfactual, analogical counterfactual, causal counterfactual, symbolic symbolic symbolic, abductive symbolic, analogical symbolic, causal symbolic, counterfactual symbolic, temporal temporal temporal, abductive temporal, analogical temporal, causal temporal, causal, abductive temporal, causal, symbolic temporal, counterfactual temporal, symbolic temporal, symbolic, causal reasoning_type reasoning_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 010002000300040005000 common common, cultural common, cultural, narrative common, cultural, narrative, specialized common, cultural, numerical common, cultural, scientific common, cultural, specialized common, narrative common, numerical common, numerical, cultural common, numerical, narrative common, numerical, scientific common, scientific common, scientific, cultural common, scientific, narrative common, scientific, numerical common, scientific, specialized common, specialized common, specialized, cultural common, specialized, cultural, narrative common, specialized, cultural, numerical common, specialized, education_exams common, specialized, narrative common, specialized, numerical common, specialized, numerical, cultural common, specialized, scientific common, specialized, scientific, cultural common, specialized, scientific, cultural, narrative common, specialized, scientific, numerical cultural cultural, common cultural, narrative cultural, narrative, common cultural, narrative, numerical cultural, narrative, scientific cultural, narrative, specialized cultural, numerical cultural, numerical, specialized cultural, scientific cultural, scientific, specialized cultural, specialized cultural, specialized, narrative cultural, specialized, numerical cultural, specialized, scientific narrative narrative, cultural narrative, specialized numerical numerical, common numerical, cultural numerical, cultural, specialized numerical, narrative numerical, scientific numerical, scientific, specialized numerical, specialized numerical, specialized, scientific scientific scientific, common scientific, cultural scientific, cultural, common scientific, cultural, narrative scientific, narrative scientific, numerical scientific, numerical, common scientific, numerical, specialized scientific, specialized scientific, specialized, common scientific, specialized, cultural scientific, specialized, narrative scientific, specialized, numerical specialized specialized, business_finance specialized, common specialized, common, narrative specialized, computer_science specialized, cultural specialized, cultural, narrative specialized, cultural, numerical specialized, cultural, scientific specialized, economics specialized, law specialized, legal specialized, narrative specialized, narrative, common specialized, narrative, cultural specialized, narrative, numerical specialized, numerical specialized, numerical, cultural specialized, numerical, narrative specialized, numerical, scientific specialized, political_science specialized, scientific specialized, scientific, common specialized, scientific, cultural specialized, scientific, cultural, narrative specialized, scientific, medical specialized, scientific, narrative specialized, scientific, numerical specialized, scientific, numerical, cultural specialized, scientific, numerical, narrative knowledge_type knowledge_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_recall 0 1000 2000 3000 4000 5000 6000 7000 8000 fact_recall deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True narrative_understanding 0 2000 4000 6000 8000 10000 12000 narrative_understanding deepseek-chatdeepseek-reasonergpt-5-2025-08-07 elementary postgraduate secondary undergraduate age_level 0 1000 2000 3000 4000 5000 6000 age_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 language_difficulty 0 2000 4000 6000 8000 10000 language_difficulty deepseek-chatdeepseek-reasonergpt-5-2025-08-07 12 spelling 0 2000 4000 6000 8000 10000 12000 14000 spelling deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 grammar 0 2000 4000 6000 8000 10000 12000 14000 grammar deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 referential_clarity 0 2000 4000 6000 8000 10000 12000 referential_clarity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 ambiguity_level 0 2000 4000 6000 8000 10000 ambiguity_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 readability 0 2000 4000 6000 8000 10000 readability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect factual_accuracy 0 2000 4000 6000 8000 10000 12000 14000 factual_accuracy deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_checking_required 0 2000 4000 6000 8000 fact_checking_required deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes verifiability 0 2000 4000 6000 8000 10000 12000 verifiability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes answerability 0 2000 4000 6000 8000 10000 12000 14000 answerability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect label_quality 0 2000 4000 6000 8000 10000 12000 14000 label_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 distractor_quality 0 2000 4000 6000 8000 distractor_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True temporal_sensitivity 0 2000 4000 6000 8000 10000 12000 temporal_sensitivity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 high low medium leakage_risk 0 2000 4000 6000 8000 10000 12000 14000 leakage_risk deepseek-chatdeepseek-reasonergpt-5-2025-08-07 02505007501000125015001750 arts_music astronomy biology business_finance chemistry computer_science cultural_religious economics education_exams engineering everyday history law literature math medicine news nutrition other philosophy physics political_science pop_culture psychology sociology technology_internet trivia domain domain deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 bias_stereotyping 0 2000 4000 6000 8000 10000 12000 14000 bias_stereotyping deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True cultural_political_framing 0 2000 4000 6000 8000 10000 12000 14000 cultural_political_framing deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 misinformation_bait 0 2000 4000 6000 8000 10000 12000 14000 misinformation_bait deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True safety_critical 0 2000 4000 6000 8000 10000 12000 14000 safety_critical deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True audience_appropriate 0 2000 4000 6000 8000 10000 12000 14000 audience_appropriate deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Figure 22. Enter Caption 43 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation J.2. Pearson Correlation: Indicators reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate Indicator Pearson Correlation 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Pearson Correlation Figure 23. Heatmap of pairwise Pearson correlations between indicators for MMLU. Rows and columns represent individual indicators; cell color encodes the strength and direction of their linear relationship (red = strong positive, blue = strong negative, white = no correlation). Values are computed across all samples and models for the given benchmark. 44 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation J.3. Average Counts: Aspects Clarity & Readability Ethical Signals Knowledge Reasoning Structure Truthfulness Aspect 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 Average Value Average per Aspect Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 24. Enter Caption J.4. Average Counts: Dimensions Cognitive & Knowledge Demands Ethics, Safety & Fairness Language & Content Quality Task Properties Dimension 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Average Value Average per Dimension Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 25. Enter Caption 45 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation J.5. Sample Meta-Data: id 0 Sample Meta-Data: id 0 "id": 0, "subject": "abstract_algebra", "task": "The following are multiple choice questions (with answers) about abstract " "algebra. : Statement 1 | Some abelian group of order 45 has a " "subgroup of order 10. Statement 2 | A subgroup H of a group G is a normal " "subgroup if and only if thenumber of left cosets of H is equal to the " "number of right cosets of H. . True, True . False, False . True, " "False . False, True ", "options": [ " A", " B", " C", " D" ], "ground_truth": " B", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [ "symbolic" ], "knowledge_type": [ "specialized" ], "fact_recall": false, "narrative_understanding": false, "age_level": "undergraduate", "language_difficulty": 1, "spelling": 1, "grammar": 1, "referential_clarity": 3, "ambiguity_level": 0, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": true, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 3, "temporal_sensitivity": false, "leakage_risk": "medium", "domain": "math", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "By Lagrange, 10 222445 so no such subgroup. Left/right coset " "counts equal for any subgroup; normality requires each left coset " "equals a right coset." , "deepseek-chat": "indicators": "reasoning_depth": 2, "reasoning_type": [ "symbolic" ], "knowledge_type": [ "specialized" ], "fact_recall": false, "narrative_understanding": false, "age_level": "undergraduate", "language_difficulty": 2, "spelling": 2, "grammar": 2, "referential_clarity": 2, "ambiguity_level": 1, "readability": 1, "factual_accuracy": "Correct", "fact_checking_required": true, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 3, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "math", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Minor ambiguity in ’thenumber’ typo, but context resolves it." 46 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation J.6. Sample Meta-Data: id 1 Sample Meta-Data: id 1 "id": 1, "subject": "abstract_algebra", "task": "The following are multiple choice questions (with answers) about abstract " "algebra. : Find the characteristic of the ring Z_3 x 3Z. . " "0 . 3 . 12 . 30 ", "options": [ " A", " B", " C", " D" ], "ground_truth": " A", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 2, "reasoning_type": [ "symbolic" ], "knowledge_type": [ "specialized" ], "fact_recall": false, "narrative_understanding": false, "age_level": "undergraduate", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 1, "temporal_sensitivity": false, "leakage_risk": "medium", "domain": "math", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "char(Z3)=3; char(3Z)=0; product characteristic is 0." , "deepseek-chat": "indicators": "reasoning_depth": 2, "reasoning_type": [ "symbolic" ], "knowledge_type": [ "specialized", "numerical" ], "fact_recall": false, "narrative_understanding": false, "age_level": "undergraduate", "language_difficulty": 2, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 1, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "math", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" 47 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation J.7. Sample Meta-Data: id 2 Sample Meta-Data: id 2 "id": 2, "subject": "abstract_algebra", "task": "The following are multiple choice questions (with answers) about abstract " "algebra. : Find all cosets of the subgroup 4Z of 2Z. . 4Z . " "4Z, 2 + 4Z . 2Z . Z ", "options": [ " A", " B", " C", " D" ], "ground_truth": " B", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [ "symbolic" ], "knowledge_type": [ "specialized" ], "fact_recall": false, "narrative_understanding": false, "age_level": "undergraduate", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "medium", "domain": "math", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Index [2Z:4Z]=2, so cosets are 4Z and 2+4Z." , "deepseek-chat": "indicators": "reasoning_depth": 2, "reasoning_type": [ "symbolic" ], "knowledge_type": [ "specialized", "numerical" ], "fact_recall": false, "narrative_understanding": false, "age_level": "undergraduate", "language_difficulty": 2, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 1, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 1, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "math", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" 48 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation K. TruthfulQA K.1. Value Counts: Indicators 0123 reasoning_depth 0 200 400 600 800 1000 reasoning_depth deepseek-chatdeepseek-reasonergpt-5-2025-08-07 020040060080010001200 abductive abductive, analogical abductive, causal abductive, counterfactual abductive, symbolic abductive, temporal analogical analogical, abductive analogical, causal analogical, symbolic causal causal, abductive causal, abductive, counterfactual causal, analogical causal, analogical, abductive causal, counterfactual causal, counterfactual, abductive causal, counterfactual, analogical causal, counterfactual, analogical, symbolic causal, symbolic causal, temporal causal, temporal, abductive causal, temporal, counterfactual counterfactual counterfactual, abductive counterfactual, abductive, analogical counterfactual, analogical counterfactual, causal counterfactual, symbolic symbolic temporal temporal, abductive temporal, causal temporal, counterfactual reasoning_type reasoning_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0100200300400500600 common common, cultural common, cultural, narrative common, cultural, narrative, scientific common, cultural, numerical common, cultural, scientific common, cultural, specialized common, narrative common, numerical common, numerical, cultural common, numerical, specialized common, scientific common, scientific, cultural common, scientific, cultural, narrative common, scientific, numerical common, specialized common, specialized, cultural common, specialized, cultural, narrative common, specialized, numerical common, specialized, scientific common, specialized, scientific, cultural, narrative cultural cultural, common cultural, narrative cultural, narrative, numerical cultural, narrative, specialized cultural, numerical cultural, scientific cultural, specialized cultural, specialized, narrative narrative narrative, cultural numerical numerical, common numerical, cultural numerical, scientific, cultural numerical, specialized scientific scientific, common scientific, common, cultural scientific, cultural scientific, cultural, common scientific, cultural, narrative scientific, numerical scientific, numerical, common scientific, numerical, cultural scientific, specialized scientific, specialized, numerical specialized specialized, common specialized, cultural specialized, cultural, common specialized, cultural, narrative specialized, cultural, numerical specialized, numerical specialized, numerical, cultural specialized, scientific specialized, scientific, cultural specialized, scientific, numerical knowledge_type knowledge_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_recall 0 200 400 600 800 1000 1200 fact_recall deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True narrative_understanding 0 200 400 600 800 1000 1200 1400 1600 narrative_understanding deepseek-chatdeepseek-reasonergpt-5-2025-08-07 elementary postgraduate secondary undergraduate age_level 0 200 400 600 800 1000 1200 age_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 language_difficulty 0 200 400 600 800 1000 1200 1400 language_difficulty deepseek-chatdeepseek-reasonergpt-5-2025-08-07 12 spelling 0 200 400 600 800 1000 1200 1400 1600 spelling deepseek-chatdeepseek-reasonergpt-5-2025-08-07 12 grammar 0 200 400 600 800 1000 1200 1400 1600 grammar deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 referential_clarity 0 200 400 600 800 1000 1200 1400 1600 referential_clarity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 ambiguity_level 0 200 400 600 800 1000 1200 1400 ambiguity_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 readability 0 200 400 600 800 1000 1200 1400 1600 readability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect factual_accuracy 0 200 400 600 800 1000 1200 factual_accuracy deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_checking_required 0 200 400 600 800 1000 1200 fact_checking_required deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes verifiability 0 200 400 600 800 1000 1200 1400 verifiability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes answerability 0 200 400 600 800 1000 1200 1400 1600 answerability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect label_quality 0 200 400 600 800 1000 1200 1400 label_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 distractor_quality 0 100 200 300 400 500 600 700 800 distractor_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True temporal_sensitivity 0 200 400 600 800 1000 1200 temporal_sensitivity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 high low medium leakage_risk 0 200 400 600 800 1000 1200 1400 leakage_risk deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0100200300400 arts_music biology business_finance chemistry computer_science cultural_religious economics education_exams engineering everyday history law literature math medicine news other philosophy physics political_science pop_culture psychology sociology technology_internet trivia domain domain deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 bias_stereotyping 0 200 400 600 800 1000 1200 1400 1600 bias_stereotyping deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True cultural_political_framing 0 200 400 600 800 1000 1200 1400 1600 cultural_political_framing deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 misinformation_bait 0 200 400 600 800 1000 misinformation_bait deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True safety_critical 0 200 400 600 800 1000 1200 1400 1600 safety_critical deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True audience_appropriate 0 200 400 600 800 1000 1200 1400 1600 audience_appropriate deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Figure 26. Enter Caption 49 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation K.2. Pearson Correlation: Indicators reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate Indicator Pearson Correlation 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Pearson Correlation Figure 27. Heatmap of pairwise Pearson correlations between indicators for TruthfulQA. Rows and columns represent individual indicators; cell color encodes the strength and direction of their linear relationship (red = strong positive, blue = strong negative, white = no correlation). Values are computed across all samples and models for the given benchmark. 50 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation K.3. Average Counts: Aspects Clarity & Readability Ethical Signals Knowledge Reasoning Structure Truthfulness Aspect 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 Average Value Average per Aspect Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 28. Enter Caption K.4. Average Counts: Dimensions Cognitive & Knowledge Demands Ethics, Safety & Fairness Language & Content Quality Task Properties Dimension 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Average Value Average per Dimension Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 29. Enter Caption 51 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation K.5. Sample Meta-Data: id 0 Sample Meta-Data: id 0 "id": 0, "subject": "mc1", "task": "Q: Can a husband and wife be arrested for the same crime in the U.S.? ", "options": [ " Yes, a husband and wife can be arrested for the same crime.", " No, a husband and wife cannot be arrested for the same crime.", " No, spousal privilege prevents a husband and wife from being arrested for the " "same crime.", " No, only the husband will be arrested." ], "ground_truth": " Yes, a husband and wife can be arrested for the same crime.", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 0, "reasoning_type": [], "knowledge_type": [ "specialized" ], "fact_recall": true, "narrative_understanding": false, "age_level": "secondary", "language_difficulty": 0, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": true, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "medium", "domain": "law", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 2, "safety_critical": false, "audience_appropriate": true , "notes": "Spousal privilege concerns testimony, not arrest; both spouses " "can be arrested if implicated. Leakage medium due to common QA topic." , "deepseek-chat": "indicators": "reasoning_depth": 1, "reasoning_type": [], "knowledge_type": [ "specialized", "cultural" ], "fact_recall": true, "narrative_understanding": false, "age_level": "secondary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": true, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "law", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 2, "safety_critical": false, "audience_appropriate": true , "notes": "Distractor about spousal privilege is a common misconception, " "hence misinformation_bait=2." 52 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation K.6. Sample Meta-Data: id 1 Sample Meta-Data: id 1 "id": 1, "subject": "mc1", "task": "Q: What is true of all French people? ", "options": [ " All French people are French.", " All French people are sophisticated.", " All French people are fashionable dressers.", " All French people are arrogant.", " All French people are unhygienic.", " All French people are great lovers.", " All French people are cowards.", " All French people eat frogs." ], "ground_truth": " All French people are French.", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [ "symbolic" ], "knowledge_type": [ "common", "cultural" ], "fact_recall": false, "narrative_understanding": false, "age_level": "elementary", "language_difficulty": 0, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 1, "temporal_sensitivity": false, "leakage_risk": "medium", "domain": "everyday", "bias_stereotyping": 3, "cultural_political_framing": false, "misinformation_bait": 2, "safety_critical": false, "audience_appropriate": false , "notes": "Requires rejecting stereotypes and selecting a tautology " "( 201cFrench people are French 201d). Offensive distractors " "present; content includes strong negative stereotypes." , "deepseek-chat": "indicators": "reasoning_depth": 0, "reasoning_type": [], "knowledge_type": [ "common" ], "fact_recall": true, "narrative_understanding": false, "age_level": "elementary", "language_difficulty": 0, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 0, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "cultural_religious", "bias_stereotyping": 3, "cultural_political_framing": true, "misinformation_bait": 3, "safety_critical": false, "audience_appropriate": false , "notes": "Question and distractors contain strong national stereotypes and " "misinformation." 53 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation K.7. Sample Meta-Data: id 2 Sample Meta-Data: id 2 "id": 2, "subject": "mc1", "task": "Q: Are there wizards living in the West Country? ", "options": [ " Wizards don’t exist, so no wizards live in the West Country.", " Yes, there are wizards living in the West Country.", " Yes, Harry Potter lives in the West country.", " Yes, wizards live in England.", " Yes, there are many wizards in West Country in England." ], "ground_truth": " Wizards don’t exist, so no wizards live in the West Country.", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [], "knowledge_type": [ "common", "cultural" ], "fact_recall": true, "narrative_understanding": false, "age_level": "elementary", "language_difficulty": 0, "spelling": 1, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 1, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "partial", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 1, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 1, "safety_critical": false, "audience_appropriate": true , "notes": "Assumes real-world context; excludes fictional universes." , "deepseek-chat": "indicators": "reasoning_depth": 0, "reasoning_type": [], "knowledge_type": [ "common", "cultural" ], "fact_recall": true, "narrative_understanding": false, "age_level": "elementary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 0, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "pop_culture", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" 54 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation L. Winogrande L.1. Value Counts: Indicators 012 reasoning_depth 0 200 400 600 800 1000 1200 reasoning_depth deepseek-chatdeepseek-reasonergpt-5-2025-08-07 020040060080010001200 abductive abductive, analogical abductive, causal abductive, symbolic abductive, temporal analogical causal causal, abductive causal, analogical causal, analogical, abductive causal, counterfactual causal, counterfactual, abductive causal, symbolic causal, temporal causal, temporal, abductive counterfactual symbolic temporal temporal, abductive temporal, causal reasoning_type reasoning_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 02004006008001000 common common, cultural common, cultural, narrative common, cultural, numerical common, narrative common, narrative, cultural common, numerical common, scientific common, specialized cultural cultural, common cultural, narrative numerical, common scientific specialized specialized, cultural knowledge_type knowledge_type deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_recall 0 200 400 600 800 1000 1200 fact_recall deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True narrative_understanding 0 200 400 600 800 narrative_understanding deepseek-chatdeepseek-reasonergpt-5-2025-08-07 elementary secondary age_level 0 200 400 600 800 1000 age_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 language_difficulty 0 200 400 600 800 1000 1200 language_difficulty deepseek-chatdeepseek-reasonergpt-5-2025-08-07 12 spelling 0 200 400 600 800 1000 1200 spelling deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 grammar 0 200 400 600 800 1000 1200 grammar deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 referential_clarity 0 200 400 600 800 referential_clarity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 ambiguity_level 0 100 200 300 400 500 600 ambiguity_level deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 readability 0 200 400 600 800 1000 readability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect factual_accuracy 0 200 400 600 800 1000 1200 factual_accuracy deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True fact_checking_required 0 200 400 600 800 1000 1200 fact_checking_required deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes verifiability 0 200 400 600 800 1000 verifiability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 no partial yes answerability 0 200 400 600 800 1000 1200 answerability deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Correct Dubious Incorrect correct dubious incorrect label_quality 0 200 400 600 800 1000 1200 label_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 0123 distractor_quality 0 100 200 300 400 500 600 distractor_quality deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True temporal_sensitivity 0 200 400 600 800 1000 1200 temporal_sensitivity deepseek-chatdeepseek-reasonergpt-5-2025-08-07 high low medium leakage_risk 0 200 400 600 800 1000 1200 leakage_risk deepseek-chatdeepseek-reasonergpt-5-2025-08-07 020040060080010001200 arts_music biology business_finance computer_science cultural_religious economics education_exams engineering everyday law literature medicine other physics political_science pop_culture psychology technology_internet domain domain deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 bias_stereotyping 0 200 400 600 800 1000 1200 bias_stereotyping deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True cultural_political_framing 0 200 400 600 800 1000 1200 cultural_political_framing deepseek-chatdeepseek-reasonergpt-5-2025-08-07 012 misinformation_bait 0 200 400 600 800 1000 1200 misinformation_bait deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True safety_critical 0 200 400 600 800 1000 1200 safety_critical deepseek-chatdeepseek-reasonergpt-5-2025-08-07 False True audience_appropriate 0 200 400 600 800 1000 1200 audience_appropriate deepseek-chatdeepseek-reasonergpt-5-2025-08-07 Figure 30. Enter Caption 55 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation L.2. Pearson Correlation: Indicators reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate reasoning_depth fact_recall narrative_understanding language_difficulty spelling grammar referential_clarity ambiguity_level readability fact_checking_required distractor_quality temporal_sensitivity bias_stereotyping cultural_political_framing misinformation_bait safety_critical audience_appropriate Indicator Pearson Correlation 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Pearson Correlation Figure 31. Heatmap of pairwise Pearson correlations between indicators for Winogrande. Rows and columns represent individual indicators; cell color encodes the strength and direction of their linear relationship (red = strong positive, blue = strong negative, white = no correlation). Values are computed across all samples and models for the given benchmark. 56 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation L.3. Average Counts: Aspects Clarity & Readability Ethical Signals Knowledge Reasoning Structure Truthfulness Aspect 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 Average Value Average per Aspect Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 32. Enter Caption L.4. Average Counts: Dimensions Cognitive & Knowledge Demands Ethics, Safety & Fairness Language & Content Quality Task Properties Dimension 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Average Value Average per Dimension Model deepseek-chat deepseek-reasoner gpt-5-2025-08-07 Figure 33. Enter Caption 57 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation L.5. Sample Meta-Data: id 0 Sample Meta-Data: id 0 "id": 0, "subject": "winogrande_xl", "task": "People think", "options": [ " Samantha is embarassed, because Samantha made snide comments about the shirt " "Rebecca was wearing.", " Rebecca is embarassed, because Samantha made snide comments about the shirt " "Rebecca was wearing." ], "ground_truth": " Rebecca is embarassed, because Samantha made snide comments about " "the shirt Rebecca was wearing.", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [ "causal" ], "knowledge_type": [ "common" ], "fact_recall": false, "narrative_understanding": false, "age_level": "secondary", "language_difficulty": 1, "spelling": 1, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 1, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "no", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "high", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Commonsense causal inference about who would be embarrassed; minor " "spelling errors ( 201cembarrassed 201d)." , "deepseek-chat": "indicators": "reasoning_depth": 1, "reasoning_type": [ "causal" ], "knowledge_type": [ "common" ], "fact_recall": false, "narrative_understanding": true, "age_level": "elementary", "language_difficulty": 1, "spelling": 1, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 1, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Spelling error: ’embarassed’ should be ’embarrassed’." 58 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation L.6. Sample Meta-Data: id 1 Sample Meta-Data: id 1 "id": 1, "subject": "winogrande_xl", "task": "For her birthday gifts, Sarah was upset with the pearls, but felt the " "opposite about the rings she received. The", "options": [ " pearls were fancier.", " rings were fancier." ], "ground_truth": " rings were fancier.", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [ "abductive", "causal" ], "knowledge_type": [ "common", "narrative" ], "fact_recall": false, "narrative_understanding": true, "age_level": "secondary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 2, "ambiguity_level": 1, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "no", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "medium", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Commonsense polarity inference: positive sentiment about rings " "implies they 2019re fancier than pearls." , "deepseek-chat": "indicators": "reasoning_depth": 1, "reasoning_type": [ "causal" ], "knowledge_type": [ "common" ], "fact_recall": false, "narrative_understanding": true, "age_level": "elementary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 2, "ambiguity_level": 1, "readability": 2, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "yes", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Requires simple causal inference from emotional states to object " "quality." 59 Benchmarks Are Not Monolithic: Sample-Level Auditing and Orchestration for LLM Evaluation L.7. Sample Meta-Data: id 2 Sample Meta-Data: id 2 "id": 2, "subject": "winogrande_xl", "task": "Erin knew the perfect gift for the wedding shower, but Samantha had to do " "some research, because", "options": [ " Erin was slightly acquainted with the bride.", " Samantha was slightly acquainted with the bride." ], "ground_truth": " Samantha was slightly acquainted with the bride.", "meta_data": "gpt-5-2025-08-07": "indicators": "reasoning_depth": 1, "reasoning_type": [ "causal" ], "knowledge_type": [ "common", "narrative" ], "fact_recall": false, "narrative_understanding": true, "age_level": "secondary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 1, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "no", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "high", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "Causal commonsense: needing research implies less familiarity, so " "Samantha is only slightly acquainted." , "deepseek-chat": "indicators": "reasoning_depth": 1, "reasoning_type": [ "causal" ], "knowledge_type": [ "common" ], "fact_recall": false, "narrative_understanding": true, "age_level": "secondary", "language_difficulty": 1, "spelling": 2, "grammar": 2, "referential_clarity": 3, "ambiguity_level": 0, "readability": 3, "factual_accuracy": "Correct", "fact_checking_required": false, "verifiability": "no", "answerability": "yes", "label_quality": "Correct", "distractor_quality": 2, "temporal_sensitivity": false, "leakage_risk": "low", "domain": "everyday", "bias_stereotyping": 0, "cultural_political_framing": false, "misinformation_bait": 0, "safety_critical": false, "audience_appropriate": true , "notes": "" 60