AI Dataset2026
Provides under-1K JSON agent-trace records documenting model refusal responses and forensic metadata — useful for evaluating refusal-detection, audit pipelines, and safety analysis; small size limits large-scale statistical studies.
AI Dataset2026
A multiple-choice benchmark for evaluating language-model arithmetic: 1,000 continuation-style elementary word problems (4 choices, balanced labels) organized by topic, grade band, and difficulty. Designed for base-model continuation log-likelihood scoring; released under Apache-2.0.
AI Dataset2026
Provides 7,000 bilingual multi-turn, search-oriented tool-use trajectories (5,000 English, 2,000 Chinese) for supervised fine-tuning and analysis of agentic search models. Includes serialized system/user/assistant messages, embedded Qwen3 tool schemas, and conversion scripts; not a standalone benchmark.
AI Dataset2026
Provides 4,868 Turkish instruction examples with explicit chain-of-thought traces (<think>...</think>) in a messages-format JSONL for training and evaluating step-by-step reasoning of Turkish LLMs; culturally localized and Apache-2.0 licensed.
AI Dataset2026
Evaluates atomic visual perception of multimodal LLMs using 3,000 short visual questions that isolate ten perceptual skills. Built from an error taxonomy across 42 benchmarks, capability-balanced and accompanied by a model leaderboard.
AI Dataset2026
A compact dataset of prompt templates and examples designed to teach LLMs to consistently report model identity fields (model ID, name, creator, family, architecture, parameter count, knowledge cutoff). Includes regex markers, usage guidance, example replacements, and a small personalization script for fine‑tuning or runtime substitution.
AI Agent Papers2026
Bridges the proprietary-to-open-source gap in agentic search by converting multi-step retrieval and reasoning traces into a structured, style-normalized JSON protocol and using it for joint distillation + RL. Produces denser supervision that improves student success rates while reducing style drift.
AI Dataset2026
Evaluates schema-guided structured extraction from documents: given a document and a JSON schema, systems must return a schema-valid JSON with page-and-box grounding. Covers 370 documents (4,869 pages) across 8 business domains and 67 document types; scores value accuracy, word/page grounding, and long-list completeness.
AI Dataset2026
Human-annotated text dataset that labels perceived “AI slop” with a continuous human slop_score (-1 / 0 / +1) plus provenance metadata (source_dataset, source_row_id, content_hash). Collected via Bench Labs SlopFinder from public datasets for training classifiers and studying subjective perception.
AI Dataset2026
Provides a machine-readable catalog of 117 AI/AX safety and deployment-readiness diagnostic criteria for assessing model intrinsic and serving/infrastructure risks. Includes MODEL-SCAN and AX-SCAN axes, bilingual source fields, per-item evidence guidance, severity/assurance metadata, and a CC BY-NC 4.0 release-candidate.
AI Dataset2026
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.
AI Dataset2026
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.