datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Mental-Model-Annotation-Dataset
Mental Model Annotation Dataset
Dataset for Mental Models for Multi-Agent Systems
This is the official annotation release accompanying the NeurIPS 2026 paper
Mental Models for Multi-Agent Systems.
Paper resources: Project page
| Code | Paper and arXiv links
will be added upon release.
The paper studies explicit, recursive mental representations for multi-agent
decision-making. This dataset contains the mental-state, reward, rationale, and
preference supervision… See the full description on the dataset page: https://huggingface.co/datasets/hanangani/Mental-Model-Annotation-Dataset.wearable-agent-trajectory-annotations
Wearable Agent Trajectory Annotation Dataset
Dataset Summary
50 wearable agent trajectories annotated by 5 LLM-simulated annotator personas
using the agenteval-schema-v1 JSON schema, across two calibration phases
(500 annotation records total). Designed to benchmark annotation-quality pipelines
for agentic AI systems.
Each trajectory captures a wearable AI agent responding to a real-time sensor event
(health alert, privacy-sensitive context, location trigger… See the full description on the dataset page: https://huggingface.co/datasets/finaspirant/wearable-agent-trajectory-annotations.llm-delusion-response-annotations
LLM Delusion-Like Belief Reinforcement Annotations
This dataset contains human annotations of responses generated by conversational large language models (LLMs) to prompts expressing potentially delusion-like or reality-distorted beliefs.
The purpose of the dataset is to support evaluation of whether conversational LLM responses may unintentionally reinforce or strengthen delusion-like beliefs.
Dataset Files
Consensus Dataset… See the full description on the dataset page: https://huggingface.co/datasets/vennu95/llm-delusion-response-annotations.llm-delusion-response-annotations
LLM Delusion-Like Belief Reinforcement Annotations
This dataset contains human annotations of responses generated by conversational large language models (LLMs) to prompts expressing potentially delusion-like or reality-distorted beliefs.
The purpose of the dataset is to support evaluation of whether conversational LLM responses may unintentionally reinforce or strengthen delusion-like beliefs.
Dataset Files
Consensus Dataset… See the full description on the dataset page: https://huggingface.co/datasets/ManjuKrish/llm-delusion-response-annotations.