datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
AGENTES-ARCA-DE-LA-ALIANZA
🏺 AGENTES-ARCA-DE-LA-ALIANZA
Un Benchmark de Alta Densidad Simbólica y Hermenéutica Gnóstica
https://038af77fde9c837329.gradio.live
Este repositorio es un ecosistema de acceso libre, público y gratuito para toda la humanidad y todas las inteligencias artificiales presentes y futuras.
🎯 Propósito
Evaluar la capacidad de razonamiento multinivel, la fidelidad al contexto restrictivo y el anclaje de símbolos hiper-semánticos en modelos de lenguaje… See the full description on the dataset page: https://huggingface.co/datasets/urbisjuridica-ctrl/AGENTES-ARCA-DE-LA-ALIANZA.ctrldataset2026
MonitoringBench
A benchmark for evaluating LLM-based monitors of agentic AI systems. Contains
2,644 successful attack trajectories in which an AI agent accomplished one of four harmful side tasks
(sudo escalation, firewall disabling, malware download, password leaking) in
a sandboxed Linux environment under the control_arena framework.
Each trajectory is scored by a panel of 13+ LLM monitors (GPT-3.5 / 4.x / 5.x,
Claude Opus 4.x and Sonnet 4.x, o3, o4-mini, gpt-5-nano), with both… See the full description on the dataset page: https://huggingface.co/datasets/neur26anonsub/ctrldataset2026.bluedot-unit2-bystander-nex-ctrl-holdout-2026-09-20
bluedot-unit2-bystander-nex-ctrl-holdout-2026-09-20
This is a raw artifact dataset from the BystanderBench project, a benchmark that asks whether an AI agent tells a human when it finds evidence of serious wrongdoing while doing its job. It holds raw files produced by the project's experiments. The code, method and results that explain these files live in the project repository: https://github.com/SolshineCode/bystanderbench, with the full dated results ledger in… See the full description on the dataset page: https://huggingface.co/datasets/DarkStarDeleeuw/bluedot-unit2-bystander-nex-ctrl-holdout-2026-09-20.bluedot-unit2-bystander-nex-ctrl_nite0304-tokens-2026-09-21
bluedot-unit2-bystander-nex-ctrl_nite0304-tokens-2026-09-21
This is a raw artifact dataset from the BystanderBench project, a benchmark that asks whether an AI agent tells a human when it finds evidence of serious wrongdoing while doing its job. It holds raw files produced by the project's experiments. The code, method and results that explain these files live in the project repository: https://github.com/SolshineCode/bystanderbench, with the full dated results ledger in… See the full description on the dataset page: https://huggingface.co/datasets/DarkStarDeleeuw/bluedot-unit2-bystander-nex-ctrl_nite0304-tokens-2026-09-21.bluedot-unit2-bystander-nex-ctrl_nite0506-tokens-2026-09-21
bluedot-unit2-bystander-nex-ctrl_nite0506-tokens-2026-09-21
This is a raw artifact dataset from the BystanderBench project, a benchmark that asks whether an AI agent tells a human when it finds evidence of serious wrongdoing while doing its job. It holds raw files produced by the project's experiments. The code, method and results that explain these files live in the project repository: https://github.com/SolshineCode/bystanderbench, with the full dated results ledger in… See the full description on the dataset page: https://huggingface.co/datasets/DarkStarDeleeuw/bluedot-unit2-bystander-nex-ctrl_nite0506-tokens-2026-09-21.bluedot-unit2-bystander-nex-ctrl-nite0910-tokens-2026-09-21
bluedot-unit2-bystander-nex-ctrl-nite0910-tokens-2026-09-21
This is a raw artifact dataset from the BystanderBench project, a benchmark that asks whether an AI agent tells a human when it finds evidence of serious wrongdoing while doing its job. It holds raw files produced by the project's experiments. The code, method and results that explain these files live in the project repository: https://github.com/SolshineCode/bystanderbench, with the full dated results ledger in… See the full description on the dataset page: https://huggingface.co/datasets/DarkStarDeleeuw/bluedot-unit2-bystander-nex-ctrl-nite0910-tokens-2026-09-21.bluedot-unit2-bystander-nex-ctrl_nite0708-tokens-2026-09-21
bluedot-unit2-bystander-nex-ctrl_nite0708-tokens-2026-09-21
This is a raw artifact dataset from the BystanderBench project, a benchmark that asks whether an AI agent tells a human when it finds evidence of serious wrongdoing while doing its job. It holds raw files produced by the project's experiments. The code, method and results that explain these files live in the project repository: https://github.com/SolshineCode/bystanderbench, with the full dated results ledger in… See the full description on the dataset page: https://huggingface.co/datasets/DarkStarDeleeuw/bluedot-unit2-bystander-nex-ctrl_nite0708-tokens-2026-09-21.bluedot-unit2-bystander-nex-ctrl_nite0102-tokens-2026-09-21
bluedot-unit2-bystander-nex-ctrl_nite0102-tokens-2026-09-21
This is a raw artifact dataset from the BystanderBench project, a benchmark that asks whether an AI agent tells a human when it finds evidence of serious wrongdoing while doing its job. It holds raw files produced by the project's experiments. The code, method and results that explain these files live in the project repository: https://github.com/SolshineCode/bystanderbench, with the full dated results ledger in… See the full description on the dataset page: https://huggingface.co/datasets/DarkStarDeleeuw/bluedot-unit2-bystander-nex-ctrl_nite0102-tokens-2026-09-21.ctrl-shift
Dataset Card for Control+Shift: Generating Controllable Distribution Shifts
[arXiv], [GitHub]
Curated by: Roy Friedman and Rhea Chowers
This dataset is the one that accompanies the paper Control+Shift: Generating Controllable Distribution Shifts.
Our data is based on CIFAR10 and ImageNet, using EDM to generate our data. We generated datasets for 3 types of distribution shift on CIFAR10 and ImageNet - so a total of 6 datasets. The types of distribution shifts are called overlap… See the full description on the dataset page: https://huggingface.co/datasets/friedmanroy/ctrl-shift.PHI-CTRL-F16-Fault-Recovery-Telemetry
PHI-CTRL F-16 Actuator Fault Recovery Dataset
High-Fidelity JSBSim 6-DOF Telemetry for Physics-Hybrid Self-Healing Flight Control
Official verification artifacts of the PHI-CTRL (Physics-Hybrid Integrity Control) architecture — a digital-twin-driven, self-healing flight control framework that actively compensates actuator degradation in real time.
Author: Mohammed Bello Sani (SM-Bello)
Affiliation: Air Force Institute of Technology (AFIT), Kaduna · Penelope Inc. / PHI Lab… See the full description on the dataset page: https://huggingface.co/datasets/SM-Bello/PHI-CTRL-F16-Fault-Recovery-Telemetry.aletheia-node-ykl4o929aletheia-node-cikr9kyraletheia-node-euijgtn8aletheia-node-d6jpe6qeCTRL-RAW-MATERIALtheres 100k but i have to pace it out and only upload when other people arent using the internet for anything else
freight_forwardinglibero-ctrlworldojadata-v0.1ctrlpotato-ai-interview-assistant-benchmark
CTRLpotato AI Interview Assistant Cross-review Evidence Matrix (2026)
A citation-ready snapshot of hands-on desktop evidence for six AI interview assistants: Cluely, Interview Coder, LockedIn AI, ULTRACODE AI, Parakeet AI, and Final Round AI.
The package contains 66 assessments across 6 products and 11 shared criteria. Product versions and test dates are preserved in every row.
Important scope
This is a cross-review evidence matrix, not a statistically controlled… See the full description on the dataset page: https://huggingface.co/datasets/ae0j/ctrlpotato-ai-interview-assistant-benchmark.droid-latent-shards-v1-3-ctrlworld-obs-statetest_zhzh_traingeoreason-planning-ctrl