10,005 single-turn prompts and metadata derived from Ox Alpha chat traces, labeled by category, subcategory, and difficulty. Built for response-teacher generation and distillation workflows, with topic and difficulty distributions to aid dataset curation.
Provides Parquet-backed pretraining subsets of web and synthetic QA text for large-language-model training, including web-high-nltk-qa, web-high-medium, and txt360-qa. Offers streaming access, provenance metadata, and CC BY 4.0 licensing; intended for LM pretraining and research.
Evaluates whether tool-using LLM agents reliably complete stateful business workflows via 507 executable agent–tool–user tasks across retail, travel, auto insurance, neobank, and IT/HR consulting. Provides browsable Parquet tables for tasks, scenarios, and agent instructions; v1.0 is intended for evaluation-only.
Provides 115,293 illustrated page images and a 975,345-row manifest sampled from scanned Encyclopaedia Britannica volumes (1768–1929), with per-page classifier probabilities for illustration — ready for image-classification, OCR-aware vision research, and illustration mining.
Hugging Face dataset for the MVA Hackathon 2026 containing pediatric rare-disease genomic data (~85 GB across 11 files). Access requires accepting dataset conditions; intended for genomic ML, variant analysis, and hackathon submissions, with notable storage and privacy constraints.
Provides large-scale mathematical problem-solving, rewriting, and dialogue data organized into five Parquet-backed subsets for reasoning-oriented language-model training. Subsets support streaming access, Dataset Viewer inspection, and per-subset provenance metadata; licensed Apache 2.0.
Provides 1.21M densely annotated desktop screenshots and 159.7M element instances for training and evaluating GUI grounding and screen-parsing models. Includes per-element accessibility-derived annotations, 917K recorded click transitions, multi-application scenes across seven appearance presets and resolutions; distributed as WebDataset shards with Parquet indexes.
Provides 22.7 hours of read Amharic speech (7,405 clips, 320 speakers) for ASR, collected via a crowdsourced Telegram bot and peer-validated; speaker- and prompt-disjoint train/validation/test splits, 16 kHz audio under CC BY 4.0.
Evaluates AI agents' ability to complete end-to-end scientific workflows by releasing and assessing 97 tasks from a 300-task FrontierChallenge suite across chemistry, materials, life science, and electrochemistry. Finds that top agent configurations achieved only a 20.6% pass rate despite high partial scores, revealing a gap between partial progress/confident completion claims and actual complete scientific deliverables.
A 10‑billion‑document retrieval benchmark with per‑document 768‑dim unit‑norm dense embeddings and mGTE sparse embeddings, FineWeb text/metadata, and exact top‑1000 MS MARCO ground truth for ~120k queries. Built for large‑scale evaluation of dense/sparse/hybrid retrieval, filtered search, indexing, ANNS algorithms, and embedding compression.
Evaluates retrieval-augmented generation by decomposing user queries into sub-queries and answers into atomic claims, scoring retrieval by query coverage and generation by claim verifiability. Reference-free benchmark with 800 queries, inlined retrieved chunks, and answers from multiple RAG systems; runs locally without API keys.
Contains 244 Cursor agent sessions recorded from Claude Fable‑5, formatted for training and research. Sessions include multi-turn assistant/tool interactions and are Teich-compatible; several rows exceed one million characters, so apply explicit tokenization and oversize policies before training.