AI Dataset2026
Provides a deduplicated 2.0M-row corpus of FABLE.5 / Mythos agent traces with row-level provenance and session-limit rows removed. Includes canonical Parquet and gzip JSONL exports, SHA256 row hashes, and provenance fields for tracing first-source datasets.
AI Dataset2026
Provides a small, manually annotated benchmark for evaluating vision–language models that convert robot and egocentric manipulation videos into timestamped subtask segments and concise action labels. Contains 100 episodes, 743 gold segments, and MP4 bytes embedded per row.
AI Dataset2026
Provides 999,847 persona records—599,847 grounded from real sources and 400,000 synthetic—each encoded as 1,290 categorical attributes packed into 645-byte Parquet blobs. Includes a codebook, postings index, and calibration/audit artifacts; decode with pyarrow and persona_codes.schema.json.
AI Dataset2026
Measures whether models produce valid JSON/YAML that strictly follow a requested schema across diverse, naturally phrased prompts. Contains 2,000 frozen test prompts with binary structural validation (no constrained decoding), focusing on schema compliance and edge cases like escaping, wrapper keys, and fenced code blocks.
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 Model2026
Performs zero-shot classification and regression on mixed numerical and categorical tabular data by treating training rows as in-context examples and predicting in a single forward pass. Uses alternating row/column attention and row compression; limited to 10 classes and model weights are non-commercial.
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 ~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
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.
AI Dataset2026
Structured dataset of internship listings combined with content-performance (SEO) metrics, provided as tabular and textual fields for data-warehouse analysis. Useful for building search/ranking features, training NLP models on internship-related queries, or performing analytics on content performance.