Scientific Reasoning Benchmark Dataset

via “visual mathematical dataset curation and annotation”

Visual mathematical reasoning benchmark.

Unique: Newly created datasets (IQTest, FunctionQA, PaperQA) are purpose-built for compositional visual-mathematical reasoning rather than repurposed from general vision-language tasks. Includes auxiliary annotations (OCR, captions) enabling evaluation of text-only models as baselines, revealing how much visual understanding contributes to performance vs. text-based reasoning alone.

vs others: More comprehensive than single-source mathematical reasoning datasets because it aggregates 28 existing datasets plus 3 new ones, providing broader coverage of visual mathematical domains and reducing bias from any single source's annotation style or problem distribution.

via “benchmark dataset for evaluating language model reasoning”

23 hardest BIG-Bench tasks where models initially failed.

Unique: Specifically curated to challenge language models on reasoning tasks rather than knowledge retrieval, making it unique in its focus.

vs others: Offers a more rigorous evaluation of reasoning capabilities compared to standard datasets that focus primarily on knowledge retrieval.

7.8K science questions testing genuine reasoning, not just recall.

Unique: This dataset uniquely challenges AI models with questions that require genuine scientific reasoning rather than simple retrieval or memorization.

vs others: It stands out from other datasets by focusing specifically on the application of scientific knowledge in novel contexts.

via “biomedical domain-specific benchmark for evaluating language model reasoning”

Biomedical QA from PubMed abstracts testing evidence-based reasoning.

Unique: Provides a standardized benchmark specifically designed for biomedical reasoning with expert-validated test set (1,000 pairs), enabling reproducible evaluation of language models on evidence-based reasoning tasks. The ternary label scheme captures nuance in biomedical evidence that binary benchmarks cannot express.

vs others: More specialized for biomedical reasoning than general QA benchmarks like GLUE or SuperGLUE, with domain-specific labels and evidence requirements that better reflect real clinical reasoning challenges

via “benchmark dataset curation and annotation for financial ai evaluation”

8.3K financial reasoning questions over real S&P 500 earnings reports.

Unique: Provides a publicly available, reproducible benchmark specifically designed for financial numerical reasoning with real SEC filings, enabling standardized comparison across different financial AI systems. Most financial datasets are proprietary or synthetic; this is open-source and authentic.

vs others: More specialized and challenging than generic QA benchmarks (SQuAD, MRQA) because it requires financial domain knowledge and multi-step arithmetic, but narrower in scope than comprehensive financial understanding benchmarks because it focuses only on numerical reasoning

via “adversarially-filtered commonsense reasoning benchmark construction”

44K pronoun resolution problems testing commonsense understanding.

Unique: Applies multi-stage adversarial filtering (automated bias detection + human validation) to remove examples solvable via statistical shortcuts, ensuring models must perform genuine semantic reasoning rather than exploiting dataset artifacts like word frequency correlations or syntactic position biases

vs others: More robust than earlier Winograd Schema Challenge (273 examples) by scaling to 44K examples while maintaining adversarial filtering, and more resistant to gaming than unfiltered pronoun resolution datasets like OntoNotes by explicitly removing statistical biases

via “commonsense reasoning benchmark dataset”

70K commonsense reasoning questions with adversarial distractors.

Unique: Utilizes adversarial filtering to ensure that incorrect options are specifically designed to mislead machines while remaining obvious to humans.

vs others: Offers a unique approach to commonsense reasoning evaluation that combines human-like accuracy with challenging adversarial examples, setting it apart from traditional datasets.

via “benchmark dataset for mathematical reasoning”

12.5K competition math problems across 7 subjects and 5 difficulty levels.

Unique: This dataset includes detailed step-by-step solutions for each problem, making it unique for training AI in mathematical reasoning.

vs others: Unlike other datasets, MATH provides a structured approach to evaluating mathematical reasoning with competition-level problems and solutions.

via “benchmark-validated reasoning performance on standardized datasets”

Alibaba's 32B reasoning model with chain-of-thought.

Unique: Provides documented benchmark results on standardized reasoning datasets (AIME 79.5%, MATH-500 96.4%) enabling quantitative performance validation, with explicit comparison claims against larger models

vs others: Demonstrates competitive reasoning performance on standardized benchmarks comparable to much larger models, providing quantitative evidence of reasoning capability for evaluation and comparison purposes

via “multi-step mathematical reasoning benchmark evaluation”

8.5K grade school math problems — multi-step reasoning, verifiable solutions, reasoning benchmark.

Unique: Uses linguistically diverse, human-authored grade school problems (not synthetic) that require genuine multi-step reasoning with basic arithmetic, combined with a standardized answer extraction format (#### delimiter) that enables reproducible evaluation across heterogeneous model outputs

vs others: More challenging than simple arithmetic benchmarks (requires 2-8 reasoning steps) yet more accessible than advanced math benchmarks, making it ideal for measuring practical reasoning improvements in production models

via “science reasoning with o1-level performance”

Open-source reasoning model matching OpenAI o1.

Unique: Claims o1-level performance on science reasoning through general-purpose RL-trained reasoning, without domain-specific training or symbolic solvers. Specific science benchmarks and methodology are undocumented.

vs others: Unknown — science benchmark performance is claimed but not quantified, making comparison to alternatives impossible.

via “mathematical reasoning with math benchmark performance”

Meta's 70B open model matching 405B-class performance.

Unique: Achieves strong mathematical reasoning performance at 70B parameters through instruction-tuning on mathematical problem-solving datasets, enabling competitive MATH benchmark performance without specialized symbolic reasoning modules

vs others: Provides mathematical reasoning capability comparable to larger closed-source models while remaining open-weight and self-hostable, though without formal verification guarantees of symbolic math systems

via “complex visual reasoning task dataset generation”

150K visual instruction examples for multimodal model training.

Unique: Largest component (77K examples) focused specifically on reasoning tasks rather than simple recognition. Uses GPT-4V to generate questions that require multi-step inference, spatial understanding, and logical reasoning over visual elements, creating a reasoning-focused instruction tuning signal.

vs others: Larger and more reasoning-focused than existing VQA datasets (GQA, OK-VQA) because it leverages GPT-4V's ability to generate diverse reasoning questions at scale; stronger training signal for reasoning than datasets with simple factual questions.

via “expert-verified question dataset with contamination detection”

Graduate-level expert QA — unsearchable questions in biology, physics, chemistry for deep reasoning.

Unique: Includes a canary string (unique identifier) embedded in each question for detecting data contamination in model training, enabling researchers to identify whether models have memorized benchmark questions. Questions are explicitly verified to be unsearchable via web search, ensuring that high performance requires genuine reasoning rather than information retrieval.

vs others: More rigorous than generic QA benchmarks because questions are expert-written and verified to be unsearchable, whereas many benchmarks (e.g., SQuAD) can be answered by simple web search or pattern matching, making them less useful for evaluating true reasoning ability.

via “multi-step reasoning evaluation”

Graduate-level science questions requiring reasoning

Unique: The benchmark's focus on graduate-level questions requiring multi-step reasoning sets it apart from simpler benchmarks like MMLU, which often focus on knowledge recall.

vs others: More rigorous than MMLU due to its emphasis on deep domain expertise and multi-step reasoning.

via “commonsense reasoning evaluation”

Commonsense NLI with adversarial context mining

Unique: Utilizes adversarially filtered questions to create plausible distractors, ensuring a more robust evaluation of reasoning capabilities compared to traditional benchmarks.

vs others: More challenging than standard commonsense benchmarks due to its focus on plausible distractors, making it a better test for true understanding.

via “reasoning capability evaluation”

Subset of BIG-Bench where most models fail

Unique: The curation of tasks specifically targeting reasoning limits rather than general performance allows for a more focused evaluation of model capabilities.

vs others: More targeted than generic benchmarks, as it specifically identifies and tests reasoning weaknesses in models.

via “commonsense reasoning evaluation through pronoun disambiguation”

Commonsense reasoning with pronoun resolution

Unique: WinoGrande's dataset is uniquely designed to challenge models on their understanding of context and semantics rather than relying on statistical patterns, making it a more rigorous test of reasoning capabilities.

vs others: More comprehensive than traditional benchmarks like Winograd Schema Challenge, as it includes a larger and more diverse set of examples.

via “commonsense-reasoning-benchmark-dataset-loading”

Dataset by Rowan. 3,02,991 downloads.

Unique: Combines video-grounded context from ActivityNet Captions with adversarially-collected wrong answers (via crowdsourcing) to create harder commonsense reasoning tasks than typical multiple-choice datasets; uses HuggingFace's streaming infrastructure for efficient loading of 300K+ examples without requiring full downloads

vs others: Larger and more adversarially-challenging than SWAG (88K examples) with better video grounding than pure text-based commonsense datasets like CommonsenseQA, while maintaining standardized HuggingFace integration for reproducible benchmarking

via “scientific-reasoning-and-domain-knowledge-synthesis”

Llama-3.3-Nemotron-Super-49B-v1.5 is a 49B-parameter, English-centric reasoning/chat model derived from Meta’s Llama-3.3-70B-Instruct with a 128K context. It’s post-trained for agentic workflows (RAG, tool calling) via SFT across math, code, science, and...

Unique: Post-trained on science-specific reasoning tasks as part of agentic workflow optimization, enabling more accurate scientific synthesis than base Llama-3.3-70B without requiring domain-specific fine-tuning

vs others: More scientifically accurate than GPT-3.5-Turbo for domain-specific questions, though less specialized than domain-specific models trained on scientific literature