datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
strategic-ttc-data
Dataset: Strategic Test-Time Compute (TTC)
This dataset contains the official experiment inference traces for the paper "Test-Time Compute Games" (arXiv:2601.21839).
It includes full model generations, token counts, and correctness verifications for various Large Language Models (LLMs) across three major reasoning benchmarks: GSM8K, AIME, and GPQA.
This data allows researchers to analyze the relationship between test-time compute and model performance without needing to re-run… See the full description on the dataset page: https://huggingface.co/datasets/Human-Centric-Machine-Learning/strategic-ttc-data.daily-oracle
Daily Oracle
📰 Project Website📝 Paper - Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle
Daily Oracle is a continuous evaluation benchmark using automatically generated QA pairs from daily news to assess how the future prediction capabilities of LLMs evolve over time.
Dataset Details
Question Type: True/False (TF) & Multiple Choice (MC)
Current Version*
Time Span: 2020.01.01 - 2026.07.18
Size: 20,376 TF questions and 18,557 MC… See the full description on the dataset page: https://huggingface.co/datasets/agentic-learning-ai-lab/daily-oracle.quantum-machine-learning-theory
Neura Parse — Quantum Machine Learning Theory: Trainability, Generalization & Learning From Quantum Data
A research-depth, proof-oriented vertical on the learning theory of quantum models and quantum data. Covers why parameterized quantum circuits train or don't (barren plateaus), what they can represent, when they generalize or provably beat classical models, and — for quantum data — how to predict properties of unknown states/channels with few measurements (classical… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-machine-learning-theory.quantum-machine-learning-models
Neura Parse — Quantum Machine Learning Models: Encodings, Kernels, QNNs & Generative/Deep Architectures
A hands-on, code-first vertical on quantum models that learn from data. Spans data encodings/feature maps, variational classifiers, quantum kernels/QSVMs, and quantum neural networks through modern generative and deep architectures (quantum GANs, circuit Born machines, quantum Boltzmann machines, QCNNs, quantum autoencoders, quantum RL, and quantum… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-machine-learning-models.DeepSeek-R1-Distill-Qwen-32B-LeaPPaper: Learning from Peers in Reasoning Models
Project Page: https://learning-from-peers.github.io/
Code: https://github.com/tongxuluo/LeaP
machine-learning-glossary-ai
📚 Machine Learning & AI Technical Glossary Dataset
Curated benchmark dataset covering core terminology, mathematical formulations, and engineering principles across Deep Learning, Transformers, and MLOps.
Maintained and documented by AheadMint.
📌 Dataset Overview
Category
Key Concepts
Reference Documentation
Neural Networks
Backpropagation, Attention, Loss Functions
AheadMint Deep Learning
Generative AI
RAG Architectures, Vector Embeddings… See the full description on the dataset page: https://huggingface.co/datasets/aheadmint/machine-learning-glossary-ai.learningbench
LearningBench: Scenario-Based Learning for Delivery Leaders
A curated dataset of 361 decision scenarios, 114 caselets, and 9 report samples designed for training and evaluating AI systems on project, programme, and service delivery management skills — now including AI in Delivery Leadership and AIOps scenarios.
Dataset Description
LearningBench provides realistic, workplace-grounded scenarios that test the judgment of project managers, programme managers, and service… See the full description on the dataset page: https://huggingface.co/datasets/shahamitkumar/learningbench.gene-reinforcement-learning-instruct
reinforcement-learning-instruct v4
Gate-passed instruction data for reinforcement-learning — published when 50 fresh examples cleared the quality bar
Kind: synthetic
Domain: reinforcement-learning
Records: 198
Created: 2026-06-19T23:14:20+00:00
SHA-256: 3393dfd6bd9adc38414885ee2f5ac35f6ce60b4c57a98c3e3f2ca78e574f1469
Pipeline: v2.0.0
Filters: {"min_quality": 0.55, "limit": 1000, "source": null, "backend": "llama", "min_judge": 0.7}
Generated by:… See the full description on the dataset page: https://huggingface.co/datasets/Gene829/gene-reinforcement-learning-instruct.Structured-Todo-Lists-for-Learning-and-Projects
Academic Task Management Dataset
Overview
100 structured todo lists for academic and personal organization. Culturally diverse with 70% Indian education context, 25% European scenarios, and 5% other Asian contexts.
Dataset Structure
{
"input": "Task description",
"output": {
"type": "todo",
"title": "List title",
"category": "academic/personal/project",
"items": [
{"task": "...", "done": false, "priority": "low/medium/high"}
]
}… See the full description on the dataset page: https://huggingface.co/datasets/Srinivasmec26/Structured-Todo-Lists-for-Learning-and-Projects.k12-digital-learning-platforms-research
K-12 Digital Learning Platforms: Research Records
227 structured records describing studies and surveys about digital learning platform
effectiveness in K-12 education. Each record carries a study title, year, type,
methodology, sample size, focus area, and a set of JSON-encoded findings and
recommendation fields.
Provenance is not verifiable - do not cite these as literature
These are structured records about studies, not the studies themselves, and their… See the full description on the dataset page: https://huggingface.co/datasets/robworks-software/k12-digital-learning-platforms-research.ai-qa-demo
AI Q&A Demo Dataset
A small dataset of AI-related question-answer pairs created for the @itaienthusiast demo notebook.
Dataset Structure
Column
Type
Description
question
string
The question
answer
string
The answer
category
string
Topic category
Usage
from datasets import load_dataset
ds = load_dataset("mayankchugh-learning/ai-qa-demo")
HILL_Learning-style_JailbreakPlease read hill_learning-style_jailbreak-dataset-license-agreement
For details, please refer to the paper: A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness
We reveal a critical safety blind spot in modern LLMs: learning-style queries, which closely resemble ordinary educational questions, can reliably elicit harmful responses.
Our HILL framework introduces a novel and systematic method for constructing such queries.
HILL achieves high attack success rates with great… See the full description on the dataset page: https://huggingface.co/datasets/gracehuggingface/HILL_Learning-style_Jailbreak.