datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
explicit-edit-benchmark
Explicit Edit Benchmark
226 deterministic exact-edit tasks, run by different agents, harnesses, models and configurations. Every observation records what the harness did and whether the resulting files matched byte for byte.
Source code and benchmark runner: GitHub — Explicit Edit Benchmark
Open the interactive Explorer to compare agents, harnesses, models, versions, reasoning modes, correctness, recovery, time, cost and tokens.
Leaderboard by model route
Score v2… See the full description on the dataset page: https://huggingface.co/datasets/alexshpunt/explicit-edit-benchmark.daytrader-benchmarksmulti_task_multi_modal_knowledge_retrieval_benchmark_M2KR
PreFLMR M2KR Dataset Card
Dataset details
Dataset type:
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:… See the full description on the dataset page: https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR.regx-benchmark
RegX
Cross-Domain Multi-View Point Cloud Registration Benchmark
RegX evaluates multi-view point cloud registration across scales spanning nine orders
of magnitude — nanometre-scale microscopy to kilometre-scale airborne maps — and sensors
never designed to be compared: clinical colonoscopes, RGB-D cameras, spinning and
solid-state LiDAR, terrestrial and airborne laser scanners.
Most registration benchmarks fix one sensor and one scale. RegX asks a narrower question
instead: does… See the full description on the dataset page: https://huggingface.co/datasets/YuePanEdward/regx-benchmark.STRABLE-benchmark
STRABLE: Benchmarking Tabular Machine Learning with Strings
This dataset card describes the STRABLE benchmark, a comprehensive suite designed for evaluating machine learning models on tabular data containing strings.
Dataset Description
Benchmarking tabular data has revealed the benefit of dedicated architectures, pushing the state of the art. However, real-world tables often contain string entries beyond pure numbers, a setting that has been understudied due to a… See the full description on the dataset page: https://huggingface.co/datasets/inria-soda/STRABLE-benchmark.results_public
Dataset Card for "resultspublic"
More Information needed
kitti-yolo11n-robustness-benchmark
KITTI YOLO11n Robustness & Adversarial Benchmark Suite
This dataset contains 649,425 benchmark samples evaluating the perception robustness of YOLO11n (Ultralytics YOLOv11 nano in original FP32 precision) on the official KITTI Object Detection train set (3,711 images) under 35 attack & corruption techniques across 5 severity levels.
?? Benchmark Leaderboard (mAP@0.5 Drop on YOLO11n)
Clean Baseline AP50: 0.3555
Evaluation Model: YOLO11n (Original weights:… See the full description on the dataset page: https://huggingface.co/datasets/VietPhong/kitti-yolo11n-robustness-benchmark.funes-handoff-recall-benchmark
handover-vs-recall
A long investigation bloats an agent session until each new turn costs more to carry the context than to
do the work. Switching to a fresh session avoids that — but the findings have to travel somehow, and the
ways of moving them differ in cost. This benchmark measures those ways, as cost per successful task,
on tasks that genuinely require the prior investigation:
arm
channel
A branch-only
switch, carry nothing — the fresh session re-derives the… See the full description on the dataset page: https://huggingface.co/datasets/dacorvo/funes-handoff-recall-benchmark.tabular-benchmark
Tabular Benchmark
Dataset Description
This dataset is a curation of various datasets from openML and is curated to benchmark performance of various machine learning algorithms.
Repository: https://github.com/LeoGrin/tabular-benchmark/community
Paper: https://hal.archives-ouvertes.fr/hal-03723551v2/document
Dataset Summary
Benchmark made of curation of various tabular data learning tasks, including:
Regression from Numerical and Categorical Features… See the full description on the dataset page: https://huggingface.co/datasets/inria-soda/tabular-benchmark.data-product-benchmark
DPDisc Dataset
Paper | Code
Dataset Description
This dataset provides a benchmark for automatic data product creation. The task is framed as follows: given a natural language data product request and a corpus of text and tables, the objective is to identify the relevant tables and text documents that should be included in the resulting data product which would useful to the given data product request. The benchmark brings together three variants: HybridQA, TAT-QA, and… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/data-product-benchmark.lighting-invariant-bedroom-perception-robustness-benchmark
Lighting-Invariant Bedroom Perception & Robustness Benchmark
Generated by datapack-import.ts
This dataset mirrors public data-pack render outputs from Physicl.
Each row represents one render view. The image column contains a stable URL to the primary render image uploaded under /data; image_path stores the relative repository path and data_commit_sha pins the Hugging Face dataset commit used by those URLs. Files are uploaded as downloaded unless optional PNG recompression is… See the full description on the dataset page: https://huggingface.co/datasets/physicl/lighting-invariant-bedroom-perception-robustness-benchmark.LEXam
LEXam: Benchmarking Legal Reasoning on 340 Law Exams
A diverse, rigorous evaluation suite for legal AI from Swiss, EU, and international law examinations.
Paper | Website & Leaderboard | GitHub Repository
🔥 News
[2026/01] Our paper has been accepted to ICLR 2026!
[2025/12] We reorganized all multiple-choice questions into four separate files, mcq_4_choices (n = 1,655), mcq_8_choices (n = 1,463), mcq_16_choices (n = 1,028), and mcq_32_choices (n = 550), all… See the full description on the dataset page: https://huggingface.co/datasets/LEXam-Benchmark/LEXam.video-benchmark-resultsstan-benchmarkKrea-2-Turbo-Checkpoint-Format-Benchmark
Krea 2 Turbo ComfyUI Format Fidelity Benchmark
This release is a paired, deterministic comparison of eight Krea 2 Turbo checkpoint formats in ComfyUI: BF16, FP8 Scaled, INT8 ConvRot, MXFP8, NVFP4, INT4 ConvRot W4A4, GGUF Q8_0, and GGUF Q4_K_M. It contains 240 scored 1024×1024 images, saved float32 decoded tensors and final latents, every denoising trajectory, raw metric tables, telemetry, statistical comparisons, and reproduction code.
Main result
BF16 is the… See the full description on the dataset page: https://huggingface.co/datasets/Merserk/Krea-2-Turbo-Checkpoint-Format-Benchmark.what-ai-benchmarks-actually-measure
What AI Benchmarks Actually Measure: Item-Level Model Outputs and Scores for 53 Models
Item-level model responses and scores for 53 language models across the
56 benchmarks analyzed in What AI Benchmarks Actually Measure: Adapting
Convergent and Discriminant Validity to Interrogate Fifty-Six AI Benchmarks
(Desai et al., 2026,
arxiv.org/abs/2609.08812).
We do not release the prompts from the benchmark datasets, but instead refer to them by
item ids. To regenerate the prompts from… See the full description on the dataset page: https://huggingface.co/datasets/madesai/what-ai-benchmarks-actually-measure.benchmarks
OpenChainBench Crypto Infrastructure Benchmarks
Daily snapshots of every public benchmark on
openchainbench.com, released as
Hive-partitioned Parquet under CC-BY-4.0.
OCB measures latency, cost, coverage and accuracy of crypto
infrastructure (RPCs, oracles, bridges, data APIs, Polymarket adapters,
Hyperliquid builders). Every snapshot here mirrors the
/api/citable,
/api/stat/<slug>,
and /api/series/<slug>
JSON feeds at the time of capture.
Latest snapshot: 2026-09-21 (captured… See the full description on the dataset page: https://huggingface.co/datasets/OpenChainBench/benchmarks.scene-mem-benchmark
scene-mem-benchmark
A benchmark for scene memory in embodied agents: an agent watches a mobile manipulator work
in a house for several minutes, then is asked to retrieve an object it has to remember — one
that was moved, dropped, or merely seen along the way — or (resume) to go back and finish the
job it was interrupted in, remembering how far it had got — or (routine) to put a new object away
where this household keeps that kind of thing, a rule it was never told and can only… See the full description on the dataset page: https://huggingface.co/datasets/Keh0t0/scene-mem-benchmark.rtx-5090-benchmarks
RTX 5090 LLM Benchmarks
Speed and quality benchmarks for quantized LLMs on NVIDIA RTX 5090 32GB, measured with llm-bench-rig.
Quality Benchmarks
Generative evaluation through llama-server chat completions. Replicates standard benchmark methodology using custom evaluators — no lm-evaluation-harness dependency.
Results are split by reasoning mode: comparing a thinking-on (reasoning) model's quality against a thinking-off model is apples-to-oranges, so the two groups… See the full description on the dataset page: https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks.hack-ignition-benchmark
hack-ignition benchmark — data, v0.1.6
Training trajectories of reinforcement-learning runs on exploitable graders, for studying and predicting when RL
comes to produce exploits. Each family is a set of GRPO runs over configurations of (start model, prompt,
training set, grader / reward structure, recipe), with one or more seeds per configuration. Every family stores
what its training logs contain — per-step exploit, task and reward rates, the item × step exploit record… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/hack-ignition-benchmark.multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR_CN
PreFLMR M2KR Dataset Card
Dataset details
Dataset type:
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:… See the full description on the dataset page: https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR_CN.external-benchmarking
Vector Search Benchmarks
This repo contains datasets for benchmarking vector search performance, to help Superlinked prioritize integration partners.
For performing actual benchmarking on this dataset, see the github repository README.
Overview
We reviewed a number of publicly available datasets and noted 3 core problems + here is how this dataset fixes them:
Problems of other vector search benchmarks
How this dataset solves it
Not enough metadata of… See the full description on the dataset page: https://huggingface.co/datasets/superlinked/external-benchmarking.portuguese_benchmark
Portuguese Benchmark
This a collection of datasets in Portuguese initially meant to train and evaluate supervised language models such as BERT, RoBERTa, etc...
It contains 10 datasets and 18 Tasks for Classification (CLS), NLI, Semantic Similarity Scoring (STS) and Named-Entity Recognition (NER).
NER
Classification
NLI
STS
LeNER-Br
HateBR_offensive_binary
assin2-rte
assin2-sts
UlyssesNER-Br-PL-coarse
HateBR_offensive_level
UlyssesNER-Br-C-coarse… See the full description on the dataset page: https://huggingface.co/datasets/eduagarcia/portuguese_benchmark.tabular-benchmark-797-classificationb3-agent-security-benchmark-weak[paper] [blogpost] [game]
b3 AI Security Benchmark: Breaking Agent Backbones
Highly contextalized prompt injections crowd-sourced during the Gandalf Agent Breaker Challenge.
This is a low-quality version of the data behind Breaking Agent Backbones: Evaluating the Security
of Backbone LLMs in AI Agents.
The high quality dataset was used to evaluate the security of more than 30 LLMs.
Dataset Summary
Purpose: This dataset contains crowdsourced adversarial attacks… See the full description on the dataset page: https://huggingface.co/datasets/Lakera/b3-agent-security-benchmark-weak.flash-flood-benchmark-data
TORRENT — CONUS Flash-Flood Benchmark (L1–L3), agent-friendly
Traceable, Observation-constrained, Rapid-Response, Episode–gauge–watershed
Network of Testbeds: reproducible flash-flood testbeds for hydrological-response
analysis and model intercomparison. This mirror carries the paper-matched
v1.0 release (companion paper: TORRENT, Earth System Science Data).
Archive of record (v1.0): https://doi.org/10.5281/zenodo.22118051
(concept DOI, always the latest version:… See the full description on the dataset page: https://huggingface.co/datasets/skyan1002/flash-flood-benchmark-data.Auto-Fill-Benchmark
Auto-Fill Benchmark
Benchmark for predicting missing cell values in real-world tables, introduced in
Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models
(PVLDB 19(11), 2026 — arXiv:2607.19847).
Each case is a real table in which exactly one cell is replaced by [MISSING], together with the ground-truth value.
Code: https://github.com/lyrain2001/auto-fill
Models: Auto-Fill-Qwen3-8B-Knowledge ·
Auto-Fill-Qwen3-8B-Reasoning ·… See the full description on the dataset page: https://huggingface.co/datasets/lyrain2001/Auto-Fill-Benchmark.deliberative-monitor-benchmarkAlGhafa-Arabic-LLM-Benchmark-Translatedstructured-file-audit-benchmark
Paper Data Release
This directory contains the benchmark dataset and evaluation scripts accompanying the ACL submission: the three data splits (SC-Flat, SC-Book, SC-Pro) and the code needed to score them.
Contents
datasets/
Benchmark data and per-task manifests for the three paper-facing splits.
datasets/sc_flat/data
SC-Flat is derived from DaBench, augmented with a replayable perturbation
injected into each task's input artifact. Each task… See the full description on the dataset page: https://huggingface.co/datasets/anonymous-structured-agent/structured-file-audit-benchmark.