{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"arc-ai2-reasoning-challenge","slug":"arc-ai2-reasoning-challenge","name":"ARC (AI2 Reasoning Challenge)","type":"dataset","url":"https://huggingface.co/datasets/allenai/ai2_arc","page_url":"https://unfragile.ai/arc-ai2-reasoning-challenge","categories":["testing-quality","rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"arc-ai2-reasoning-challenge__cap_0","uri":"capability://data.processing.analysis.grade.school.science.question.benchmark.evaluation","name":"grade-school science question benchmark evaluation","description":"Provides a curated dataset of 7,787 multiple-choice science questions spanning physics, chemistry, biology, and earth science at grade-school difficulty levels. Questions are structured with a stem, four answer choices, and a correct answer label. The dataset enables systematic evaluation of LLM reasoning capabilities by measuring accuracy on questions that require applying scientific knowledge to novel scenarios rather than surface-level fact retrieval or word co-occurrence matching.","intents":["Evaluate whether my LLM can apply scientific reasoning to unfamiliar problem contexts","Benchmark my model's performance against a standardized science reasoning task","Identify gaps in my model's understanding of physics, chemistry, biology, and earth science domains","Compare my model's reasoning capabilities to published baselines on a widely-adopted benchmark"],"best_for":["LLM researchers evaluating reasoning capabilities across model families","Teams building science tutoring or educational AI systems","Organizations benchmarking proprietary models against public standards","ML engineers validating that fine-tuning improves scientific reasoning"],"limitations":["Limited to multiple-choice format — does not evaluate free-form explanation generation or step-by-step reasoning articulation","Grade-school difficulty ceiling — does not assess advanced undergraduate or professional-level science reasoning","Static snapshot — does not include temporal evaluation of how model performance changes with retraining or fine-tuning","No built-in stratification by reasoning type — cannot isolate performance on causal reasoning vs. analogical reasoning vs. quantitative reasoning","Challenge set is relatively small (2,590 questions) — may have high variance in per-domain performance estimates"],"requires":["Hugging Face Datasets library (datasets>=2.0.0) or direct JSON/CSV parsing capability","Python 3.7+ for programmatic dataset loading","LLM inference framework capable of multiple-choice classification (e.g., vLLM, Ollama, OpenAI API, Anthropic API)","Evaluation harness to compute accuracy metrics and optional domain-level breakdowns"],"input_types":["question stem (text)","four answer choices (text)","optional context or supporting information (text)"],"output_types":["predicted answer choice (single letter: A, B, C, or D)","accuracy metric (float 0.0-1.0)","per-domain accuracy breakdown (dict: domain → accuracy)","per-difficulty accuracy breakdown (dict: Easy/Challenge → accuracy)"],"categories":["data-processing-analysis","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arc-ai2-reasoning-challenge__cap_1","uri":"capability://data.processing.analysis.multi.domain.science.knowledge.assessment","name":"multi-domain science knowledge assessment","description":"Stratifies 7,787 questions across four distinct science domains (physics, chemistry, biology, earth science) with balanced representation in both Easy and Challenge subsets. This domain-level organization enables fine-grained analysis of where models succeed or fail within specific scientific disciplines. The dataset structure supports computing per-domain accuracy metrics, identifying domain-specific knowledge gaps, and detecting whether models exhibit uneven reasoning capabilities across scientific fields.","intents":["Identify which science domains my model struggles with most","Evaluate whether my model has balanced knowledge across physics, chemistry, biology, and earth science","Debug whether poor overall performance is driven by weakness in one domain or distributed across all domains","Validate that domain-specific fine-tuning improves performance in target domains without degrading others"],"best_for":["Science education AI teams building domain-specific tutoring systems","Researchers analyzing whether LLMs exhibit domain-specific reasoning biases","Teams optimizing model selection for science-heavy applications (e.g., homework help, exam prep)","Organizations conducting ablation studies on domain-specific training data"],"limitations":["Domain labels are coarse-grained — no sub-domain stratification (e.g., mechanics vs. thermodynamics within physics)","No explicit reasoning-type taxonomy — cannot isolate whether errors stem from conceptual misunderstanding vs. calculation errors vs. reading comprehension","Domain distribution may not reflect real-world question frequencies in educational settings","No metadata on question difficulty within domains — cannot assess whether models struggle uniformly or on harder questions within each domain"],"requires":["Dataset loader that preserves domain labels (Hugging Face Datasets or custom parsing)","Evaluation script capable of grouping results by domain and computing per-domain metrics","Optional: visualization library (matplotlib, seaborn) for domain-level performance comparison"],"input_types":["question stem with domain label (text + categorical)","four answer choices (text)"],"output_types":["per-domain accuracy (dict: physics/chemistry/biology/earth-science → float)","per-domain error analysis (dict: domain → list of misclassified question IDs)","domain-level confusion matrix (optional, for multi-class domain prediction)"],"categories":["data-processing-analysis","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arc-ai2-reasoning-challenge__cap_2","uri":"capability://data.processing.analysis.reasoning.difficulty.stratification.easy.vs.challenge","name":"reasoning difficulty stratification (easy vs. challenge)","description":"Partitions the dataset into two difficulty tiers: Easy (5,197 questions, solvable by retrieval and word co-occurrence baselines) and Challenge (2,590 questions, resistant to shallow methods). The Challenge subset was explicitly curated by filtering out questions that simple baseline methods could answer correctly, ensuring that remaining questions require multi-step reasoning, knowledge synthesis, or novel application of scientific principles. This two-tier structure enables evaluation of both baseline reasoning capability and advanced reasoning performance.","intents":["Measure my model's performance on questions that require genuine reasoning vs. those solvable by pattern matching","Identify whether my model's improvements come from better reasoning or just better retrieval/memorization","Evaluate whether my model has reached saturation on Easy questions and needs harder evaluation","Compare my model's reasoning gap (Easy accuracy - Challenge accuracy) to published baselines"],"best_for":["Researchers studying the reasoning capabilities of LLMs vs. retrieval-based systems","Teams building reasoning-focused evaluation suites that exclude shallow-method-solvable questions","Organizations tracking whether model improvements are driven by genuine reasoning advances","Educators assessing whether AI tutoring systems can handle non-trivial problem-solving"],"limitations":["Difficulty stratification is binary — no fine-grained difficulty spectrum (e.g., 1-5 scale)","Challenge set curation is based on specific baseline methods (retrieval + word co-occurrence) — may not generalize to newer shallow methods","No explicit reasoning-type labels within Challenge set — cannot distinguish between causal reasoning, analogical reasoning, quantitative reasoning, etc.","Challenge set is smaller (2,590 vs. 5,197) — may have higher statistical variance in performance estimates"],"requires":["Dataset loader that preserves Easy/Challenge split labels","Evaluation harness capable of computing separate accuracy metrics for each subset","Optional: statistical significance testing (e.g., bootstrap confidence intervals) for comparing Easy vs. Challenge performance"],"input_types":["question with difficulty label (text + categorical: Easy/Challenge)","four answer choices (text)"],"output_types":["Easy subset accuracy (float 0.0-1.0)","Challenge subset accuracy (float 0.0-1.0)","reasoning gap metric (Easy accuracy - Challenge accuracy)","per-subset error analysis (list of misclassified question IDs grouped by difficulty)"],"categories":["data-processing-analysis","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arc-ai2-reasoning-challenge__cap_3","uri":"capability://data.processing.analysis.standardized.multiple.choice.evaluation.harness","name":"standardized multiple-choice evaluation harness","description":"Provides a structured multiple-choice format (question stem + four answer choices + correct answer label) that enables direct integration with standard LLM evaluation pipelines. Each question is formatted consistently with a unique identifier, allowing reproducible evaluation across different models and runs. The format supports both direct accuracy computation (comparing predicted choice to ground truth) and probabilistic evaluation (ranking answer choices by model confidence scores). This standardization enables fair comparison across heterogeneous models and evaluation frameworks.","intents":["Evaluate my LLM using a standard multiple-choice format without custom parsing or data transformation","Compare my model's performance to published baselines that use the same dataset format","Integrate ARC into my existing evaluation pipeline without custom data wrangling","Compute confidence-calibrated metrics (e.g., ranking accuracy, log-loss) beyond simple accuracy"],"best_for":["LLM evaluation teams with existing multiple-choice evaluation infrastructure","Researchers comparing models using standardized benchmarks","Organizations building model leaderboards or evaluation dashboards","Teams conducting meta-analysis across multiple benchmarks with consistent formats"],"limitations":["Multiple-choice format limits evaluation to classification accuracy — does not assess explanation quality, reasoning transparency, or step-by-step problem-solving","Four-choice format may not match all LLM evaluation frameworks (some expect binary or N-way classification with different numbers of options)","No built-in support for partial credit or reasoning-based scoring — only binary correct/incorrect per question","Question IDs are not guaranteed to be stable across dataset versions — reproducibility requires pinning to specific dataset version"],"requires":["LLM inference API or local model capable of generating text or logits for four answer choices","Evaluation script that maps model outputs to answer choices (A, B, C, D) and compares to ground truth","Optional: logit extraction capability for confidence-based metrics (requires model that exposes token logits)"],"input_types":["question stem (text)","four answer choices labeled A, B, C, D (text)","optional context or supporting information (text)"],"output_types":["predicted answer choice (A/B/C/D)","accuracy (binary: correct/incorrect)","optional: confidence scores per choice (float 0.0-1.0)","optional: log-loss or cross-entropy loss (float)"],"categories":["data-processing-analysis","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arc-ai2-reasoning-challenge__cap_4","uri":"capability://data.processing.analysis.baseline.performance.comparison.and.leaderboard.anchoring","name":"baseline performance comparison and leaderboard anchoring","description":"Includes published baseline results from retrieval-based systems, word co-occurrence methods, and various LLM families (GPT-3, BERT, RoBERTa, etc.), enabling direct performance comparison and leaderboard positioning. The dataset documentation provides accuracy metrics for standard baselines, allowing new models to be evaluated against established reference points. This anchoring enables researchers to contextualize their model's performance and identify whether improvements represent genuine advances or marginal gains.","intents":["Understand how my model's performance compares to published baselines and state-of-the-art","Determine whether my model's accuracy represents a meaningful improvement or statistical noise","Position my model on the ARC leaderboard relative to other published models","Identify whether my model outperforms or underperforms relative to its size/capability class"],"best_for":["Researchers publishing new models and needing standard comparison points","Teams building model leaderboards or benchmark tracking systems","Organizations evaluating whether to adopt a new model based on ARC performance","Academics writing papers that require contextualization of results"],"limitations":["Baseline results may be outdated — published baselines are from 2018-2021, newer models (GPT-4, Claude 3, Llama 3) may have significantly different performance profiles","Baseline results may not account for prompt engineering or few-shot learning — published accuracies may not be directly comparable if different prompting strategies were used","No confidence intervals or error bars on baseline results — cannot assess statistical significance of improvements","Baseline results are typically reported on the full dataset — per-domain or per-difficulty breakdowns may not be available for all baselines"],"requires":["Access to published baseline results (typically from Allen AI's original ARC paper or Hugging Face dataset card)","Evaluation script that computes the same metrics as published baselines (e.g., accuracy on full dataset, Easy subset, Challenge subset)","Optional: statistical testing framework to assess whether new results significantly differ from baselines"],"input_types":["model predictions on ARC questions (text or logits)","published baseline results (metadata from dataset documentation)"],"output_types":["accuracy comparison table (model → accuracy on Easy/Challenge/Full)","percentile ranking relative to baselines (float 0-100)","improvement over baseline (float, percentage points)","optional: statistical significance test result (p-value)"],"categories":["data-processing-analysis","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arc-ai2-reasoning-challenge__cap_5","uri":"capability://data.processing.analysis.cross.model.reasoning.capability.comparison","name":"cross-model reasoning capability comparison","description":"Enables systematic comparison of reasoning capabilities across different model architectures, sizes, and training approaches by providing a standardized evaluation surface. The dataset's reasoning-focused curation (Challenge set) and domain stratification allow researchers to isolate which models excel at reasoning vs. retrieval, which domains each model struggles with, and how reasoning capability scales with model size. This supports meta-analysis of how architectural choices, training data, and fine-tuning affect reasoning performance.","intents":["Compare reasoning capabilities across different LLM families (GPT, Claude, Llama, Mistral, etc.)","Determine whether larger models consistently outperform smaller models on reasoning tasks","Identify whether instruction-tuned models outperform base models on science reasoning","Analyze whether models trained on different data distributions (e.g., code-heavy vs. text-heavy) have different reasoning profiles"],"best_for":["Researchers conducting model comparison studies or meta-analyses","Organizations evaluating which model family to adopt for science-heavy applications","Teams analyzing how model size, architecture, and training affect reasoning","Academics studying the relationship between model capabilities and reasoning performance"],"limitations":["Comparison is limited to accuracy — does not assess reasoning transparency, explanation quality, or failure modes","No built-in support for controlling confounding variables (e.g., different prompting strategies, different inference parameters across models)","Challenge set is relatively small (2,590 questions) — per-model performance estimates may have high variance, especially for domain-level breakdowns","No temporal tracking — cannot assess whether model performance changes with retraining, fine-tuning, or new versions"],"requires":["Inference APIs or local model deployments for multiple model families","Standardized evaluation script that applies identical prompting and inference parameters across all models","Statistical analysis framework (e.g., scipy, statsmodels) for comparing performance distributions"],"input_types":["question stem and answer choices (text)","model identifiers and inference parameters (metadata)"],"output_types":["per-model accuracy (dict: model_name → accuracy)","per-model per-domain accuracy (dict: model_name → dict: domain → accuracy)","per-model per-difficulty accuracy (dict: model_name → dict: Easy/Challenge → accuracy)","statistical comparison results (e.g., p-values for pairwise model comparisons)"],"categories":["data-processing-analysis","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arc-ai2-reasoning-challenge__cap_6","uri":"capability://data.processing.analysis.science.domain.knowledge.assessment.for.educational.ai","name":"science domain knowledge assessment for educational ai","description":"Provides a curated evaluation dataset for educational AI systems (tutoring bots, homework helpers, exam prep tools) to assess whether they can correctly answer grade-school science questions across multiple domains. The dataset's focus on applying knowledge to novel situations (rather than fact recall) aligns with educational learning objectives. Integration with educational platforms enables tracking student performance, identifying knowledge gaps, and validating that tutoring systems provide accurate guidance.","intents":["Validate that my tutoring bot provides correct answers to science questions before deploying to students","Identify which science domains my educational AI system struggles with and needs improvement","Benchmark my tutoring system's performance against published baselines to ensure quality","Track whether my tutoring system's performance improves with fine-tuning on educational data"],"best_for":["EdTech companies building science tutoring or homework help systems","Educational institutions validating AI tutoring systems before student deployment","Researchers studying how LLMs perform on educational tasks","Teams building exam prep or standardized test practice tools"],"limitations":["Grade-school difficulty level — does not assess advanced high school, AP, or college-level science reasoning","Multiple-choice format — does not evaluate free-form explanation generation or step-by-step problem-solving, which are important for tutoring","No pedagogical metadata — questions lack information about learning objectives, prerequisite knowledge, or common misconceptions","No student interaction data — cannot assess how tutoring systems explain answers or adapt to student questions","Static evaluation — does not capture whether tutoring systems can handle follow-up questions, clarifications, or alternative explanations"],"requires":["LLM inference capability (local or API-based)","Integration with educational platform or tutoring system","Optional: logging and analytics framework to track performance over time and by student cohort"],"input_types":["question stem and answer choices (text)","optional: student metadata (grade level, prior performance, learning objectives)"],"output_types":["predicted answer (A/B/C/D)","accuracy (binary: correct/incorrect)","optional: confidence score (float 0.0-1.0)","optional: explanation or reasoning (text, if system generates it)"],"categories":["data-processing-analysis","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arc-ai2-reasoning-challenge__cap_7","uri":"capability://data.processing.analysis.fine.tuning.validation.and.domain.specific.model.optimization","name":"fine-tuning validation and domain-specific model optimization","description":"Enables evaluation of whether fine-tuning on science-specific data improves model performance on reasoning tasks. The dataset's domain stratification (physics, chemistry, biology, earth science) and difficulty split (Easy/Challenge) allow researchers to measure whether fine-tuning improves performance uniformly across domains or creates domain-specific improvements. This supports iterative model optimization, ablation studies, and validation that fine-tuning generalizes to unseen science questions.","intents":["Measure whether fine-tuning my model on science data improves ARC performance","Determine whether fine-tuning improves performance uniformly or creates domain-specific improvements","Validate that fine-tuning on one science domain doesn't degrade performance on other domains","Compare the effectiveness of different fine-tuning strategies (e.g., instruction-tuning vs. in-context learning) on reasoning tasks"],"best_for":["Teams building science-specific LLMs or fine-tuning base models for science applications","Researchers studying how domain-specific training affects reasoning capability","Organizations optimizing models for science-heavy use cases (education, research, technical support)","ML engineers conducting ablation studies on training data composition"],"limitations":["Evaluation is limited to accuracy — does not assess whether fine-tuning improves reasoning transparency or explanation quality","No built-in support for tracking training dynamics (e.g., learning curves, convergence behavior) — requires external logging","Challenge set is relatively small (2,590 questions) — per-domain performance estimates may have high variance, making it difficult to detect small improvements","No control for data leakage — if fine-tuning data overlaps with ARC questions, results will be inflated","Static evaluation — does not assess whether fine-tuned models maintain performance on non-science tasks (catastrophic forgetting)"],"requires":["Base model and fine-tuning framework (e.g., Hugging Face Transformers, vLLM, Ollama)","Science-specific training data (optional, but recommended for meaningful fine-tuning)","Evaluation script that computes accuracy before and after fine-tuning, with per-domain and per-difficulty breakdowns","Optional: statistical testing framework to assess whether improvements are significant"],"input_types":["question stem and answer choices (text)","optional: fine-tuning data (text, for training)"],"output_types":["pre-fine-tuning accuracy (float 0.0-1.0)","post-fine-tuning accuracy (float 0.0-1.0)","improvement (float, percentage points)","per-domain improvement (dict: domain → improvement)","per-difficulty improvement (dict: Easy/Challenge → improvement)"],"categories":["data-processing-analysis","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"arc-ai2-reasoning-challenge__headline","uri":"capability://testing.quality.scientific.reasoning.benchmark.dataset","name":"scientific reasoning benchmark dataset","description":"A comprehensive dataset of grade-school science questions designed to evaluate AI's ability to apply scientific reasoning, rather than mere recall, making it essential for LLM evaluation.","intents":["best science reasoning dataset","dataset for evaluating AI scientific knowledge","top benchmark for LLM scientific reasoning","AI dataset for grade-school science questions","science question dataset for AI testing"],"best_for":["evaluating AI models on scientific reasoning"],"limitations":["limited to grade-school science topics"],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality","rag-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["Hugging Face Datasets library (datasets>=2.0.0) or direct JSON/CSV parsing capability","Python 3.7+ for programmatic dataset loading","LLM inference framework capable of multiple-choice classification (e.g., vLLM, Ollama, OpenAI API, Anthropic API)","Evaluation harness to compute accuracy metrics and optional domain-level breakdowns","Dataset loader that preserves domain labels (Hugging Face Datasets or custom parsing)","Evaluation script capable of grouping results by domain and computing per-domain metrics","Optional: visualization library 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