AI Dataset2026
Provides SimReady 3D assets for robotics and industrial simulation. The OpenUSD content is prepared for simulation workflows, helping teams assemble environments for physical AI.
AI Dataset2026
Provides 300 adversarial "metacognitive-trap" problems to measure whether LLMs notice and recover from their own reasoning errors. Combines multiple-choice vulnerability tests with free-form adapter-gain evaluation and ships per-model metacognition adapters for frozen-base probing.
AI Dataset2026
Provides synchronized four-perspective Rocket League match recordings with per-frame H.264 video, player action streams, event logs, and privileged physics state — released as WebDataset shards in a ~4,000-hour slice (1,000 match-hours × 4 perspectives). Includes 720p@20fps video, multi-hot keyboard actions, and CC BY-NC-SA-4.0 license.
AI Dataset2026
Provides ~494.7 hours of trimmed native PC/console gameplay screen recordings organized by game, with per-session clips plus input and per-frame event annotations. Each workflow includes clip.mp4, events.json, frame_events.json, and metadata — suitable for training vision-action, behavior-cloning, and gameplay understanding models.
AI Dataset2026
Provides 2,165 historical natural-history page scans paired with ~99.95% expert transcriptions and pixel-aligned PAGE XML layout ground truth for OCR and layout evaluation; multilingual (EN/FR/DE/LA), CC-BY 3.0.
AI Dataset2026
Provides ~50M multimodal annotations organized for unified training across structured visual understanding, segmentation, dense geometric prediction, and multi-view reconstruction — released as task-specific JSONL files that reference original image assets rather than redistributing raw images.
AI Dataset2026
Tests whether language models can solve original, culturally grounded riddles—not just translate them. It spans 62 communities and combines model answers with native-speaker evaluation; use is restricted to evaluation.
AI Dataset2026
Provides 16M+ instruction–response samples and ~81 GB (7,090 compressed GitHub repos) distilled from 68 open-source sources, organized into 8 categories for SFT, coding agents and reasoning research. Model-generated content; released as a curated MIT-licensed collection.
AI Dataset2026
Provides anonymized multi-domain user behavior sequences and content metadata (short video, ads, e-commerce, live) for cross-domain recommendation, semantic-ID mapping, and content-understanding tasks. Key tables include per-user multi-domain behavior (~500k rows), pid→three-segment semantic IDs, captions, and level-3 tags; all item IDs are hashed for privacy.
AI Dataset2026
Provides 100,891 JSON-formatted agent conversation examples where each assistant turn includes a short <think> internal reasoning trace before tool/function calls. Human-facing text and tool calls are preserved; intended to fine-tune models to produce concise, cost-efficient chain-of-thought for tool use.
AI Dataset2026
Converts long or messy model reasoning traces into concise, user-facing summaries with optional metadata. 61K cleaned English samples in JSON format, Apache-2.0 licensed, created to train and evaluate reasoning-summarization models and to present safe, readable explanations instead of raw chain-of-thought.
AI Dataset2026
Provides labeled prompts with full-reference answers (including chain-of-thought and code blocks) and per-example metadata to train edge routing/orchestrator models that decide whether to handle inputs locally or route them to larger models. Includes complexity scores, coding/math flags, routing justifications, and an automated override rule; suited for fine-tuning small models (50M–1.5B) for edge deployment.