datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
submission-eval-artifacts
NeurIPS ED 2026 Anonymous Evaluation Artifacts
This dataset repo contains sanitized evaluation artifacts for an anonymous NeurIPS ED 2026 submission. It is metadata-focused: normalized benchmark JSON, selected small paper-facing summaries, reviewer indexes, and manifests.
Checkpoint artifacts are referenced through neurips-ed2026-anon-checkpoints/submission-checkpoints. This dataset repo does not contain model checkpoints or model weights.
Anonymous code artifact:… See the full description on the dataset page: https://huggingface.co/datasets/neurips-ed2026-anon-checkpoints/submission-eval-artifacts.ArtiFact
ArtiFact
ArtiFact is a large-scale multimodal benchmark of museum artwork records with aligned images and structured metadata. It is designed for evaluating metadata extraction, error detection, semantic querying, and multimodal reasoning over cultural-heritage collections.
The dataset combines records from the Rijksmuseum, the Metropolitan Museum of Art (Met), and the Art Institute of Chicago (AIC), with normalized fields for artists, dates, materials, techniques, dimensions… See the full description on the dataset page: https://huggingface.co/datasets/deem-data/ArtiFact.magic-video-artifacts
MAGIC-Video — Preprocessing Artifacts
This dataset hosts the exact preprocessing artifacts used in the paper
"Bridging Modalities, Spanning Time: Structured Memory for Ultra-Long Agentic Video Reasoning"
(MAGIC-Video, arXiv:2605.08271).
Why release these?
The paper's preprocessing pipeline calls LLMs through OpenRouter (translation, OpenIE, semantic
consolidation, narrative chain distillation). Those calls cost money, take hours per subject,
and are non-deterministic — re-running… See the full description on the dataset page: https://huggingface.co/datasets/jiazhengli7/magic-video-artifacts.forecastgen-artifacts
Forecast-Generalization: raw evaluation outputs across 38 reasoning models
Complete generation-level outputs, per-seed scores and analysis artifacts from a
study of how well benchmark performance forecasts generalization to held-out
reasoning tasks.
Most released evaluations report only aggregate accuracy. This release keeps the
raw per-problem, per-seed generations, so item-level analyses can be redone
without re-running any inference.
What is here
38 models… See the full description on the dataset page: https://huggingface.co/datasets/dvader13/forecastgen-artifacts.mhqa-itu-artifacts
MHQA · ITU · Zindi Challenge — Artifacts
DariusTheGeek/mhqa-itu-artifacts · the data + precomputed features that let the code repo reproduce
submission sub_v40 (public LB 0.728509) for the ITU Multilingual Health QA in Low-Resource African
Languages challenge. Code (which pulls this at runtime) lives on GitHub; trained weights are in the model repo
DariusTheGeek/mhqa-itu-adapters.
This is a reproducibility artifact bundle, not a raw dataset. It holds derived features and the… See the full description on the dataset page: https://huggingface.co/datasets/DariusTheGeek/mhqa-itu-artifacts.caliper-artifact
CALIPER Artifact
This repository contains the anonymous artifact release for CALIPER, a prompt-robustness dataset and benchmark built from Alpaca, GSM8K, and MMLU prompts. It includes prompt paraphrases, style tags, generated model responses, automated content-preservation scores, automated response-quality scores, manual audit files, analysis scripts, paper figures, and Croissant/Responsible AI metadata.
Layout
data/
alpaca/
gsm8k/
mmlu/
paraphrases_tagged.json… See the full description on the dataset page: https://huggingface.co/datasets/idacy/caliper-artifact.Execution-Bound-Artifact-Reconstruction-Layer
🚩 Γ Physics Engine — Canonical Definition
Γ 物理引擎創建者 & 公式創始者：熊網區塊鏈 (BearNetworkChain) 創辦人 陳霆
最早提出時間：2025 年 6 月 19 日
原始來源：https://www.facebook.com/share/p/19cadcMTGo/
Chen, Ting. (2026). BearNetworkchain Execution Specification. Zenodo
📌 0. 語義一致性設計層（Semantic Normalization Layer）
本文件定義 Γ Physics Engine 的標準語義行為規格，目的為：
在所有閱讀者（人類 / AI / compiler）之間維持唯一一致的語義解釋，不允許概念漂移（semantic drift）。
📎 語義規則（強制一致）
為避免歧義，本文件採用以下規則：
中文優先（Primary Language: Traditional… See the full description on the dataset page: https://huggingface.co/datasets/BNES-BRNKC/Execution-Bound-Artifact-Reconstruction-Layer.Execution-Bound-Artifact-Reconstruction-Layer
🚩 Γ Physics Engine — Canonical Definition
Γ 物理引擎創建者 & 公式創始者：熊網區塊鏈 (BearNetworkChain) 創辦人 陳霆
最早提出時間：2025 年 6 月 19 日
原始來源：https://www.facebook.com/share/p/19cadcMTGo/
Chen, Ting. (2026). BearNetworkchain Execution Specification. Zenodo
📌 0. 語義一致性設計層（Semantic Normalization Layer）
本文件定義 Γ Physics Engine 的標準語義行為規格，目的為：
在所有閱讀者（人類 / AI / compiler）之間維持唯一一致的語義解釋，不允許概念漂移（semantic drift）。
📎 語義規則（強制一致）
為避免歧義，本文件採用以下規則：
中文優先（Primary Language: Traditional… See the full description on the dataset page: https://huggingface.co/datasets/BearNetworkChain/Execution-Bound-Artifact-Reconstruction-Layer.caliper-artifact
CALIPER Dataset
This repository contains the anonymous dataset release for CALIPER, a prompt-robustness dataset and benchmark built from Alpaca, GSM8K, and MMLU prompts. It includes the canonical CALIPER dataset files, metadata, samples, analysis outputs, figures, and Croissant/Responsible AI metadata.
Code and analysis scripts are hosted separately in the anonymous GitHub artifact repository:
https://github.com/caliper-artifact/caliper-artifact
The interactive CALIPER… See the full description on the dataset page: https://huggingface.co/datasets/caliper-artifact/caliper-artifact.legal-vn-hackaithon-artifact
Legal VN HackAIthon — Processed Artifacts
Large artifacts for Legal Retrieval — HackAIthon 2026 (Track C INNOVATOR).Source code, small train/test files, and run instructions live on GitHub:
https://github.com/VanHung-05/legal-vn-hackaithon-2026
Overview
This repository is not the official BTC train/test release. It stores processed Vietnamese legal corpus artifacts:
Raw corpus flattened into law articles (articles.jsonl)
Dense embeddings for the full corpus… See the full description on the dataset page: https://huggingface.co/datasets/nguyenvanhung05/legal-vn-hackaithon-artifact.