datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
multi-agent-scam-conversation
Synthetic Multi-Turn Scam and Non-Scam Phone Conversation Dataset with Agentic Personalities
Dataset Description
The Synthetic Multi-Turn Scam and Non-Scam Phone Dialogue Dataset with Agentic Personalities is an enhanced collection of simulated phone conversations between two AI agents, one acting as a scammer or non-scammer and the other as an innocent receiver. Each dialogue is labeled as either a scam or non-scam interaction. This dataset is designed to help develop… See the full description on the dataset page: https://huggingface.co/datasets/BothBosu/multi-agent-scam-conversation.Multi-Agent_Reinforcement_Learning_Trading_System_Data
📊 Multi-Agent RL Trading System - Dataset
This dataset contains historical OHLCV (Open, High, Low, Close, Volume) data for AAPL, MSFT, and GOOGL, pre-processed for Reinforcement Learning based trading systems.
📁 Dataset Content
The dataset consists of CSV files downloaded via yfinance:
AAPL.csv: Apple Inc. daily data (Jan 2018 - Dec 2024).
MSFT.csv: Microsoft Corp. daily data (Jan 2018 - Dec 2024).
GOOGL.csv: Alphabet Inc. daily data (Jan 2018 - Dec 2024).
📝… See the full description on the dataset page: https://huggingface.co/datasets/AdityaaXD/Multi-Agent_Reinforcement_Learning_Trading_System_Data.Multi-Agent_Reinforcement_Learning_Trading_System_Data
📊 Multi-Agent RL Trading System - Dataset
This dataset contains historical OHLCV (Open, High, Low, Close, Volume) data for AAPL, MSFT, and GOOGL, pre-processed for Reinforcement Learning based trading systems.
📁 Dataset Content
The dataset consists of CSV files downloaded via yfinance:
AAPL.csv: Apple Inc. daily data (Jan 2018 - Dec 2024).
MSFT.csv: Microsoft Corp. daily data (Jan 2018 - Dec 2024).
GOOGL.csv: Alphabet Inc. daily data (Jan 2018 - Dec 2024).… See the full description on the dataset page: https://huggingface.co/datasets/sanjaydoss/Multi-Agent_Reinforcement_Learning_Trading_System_Data.multi-agent-scam-conversation
Synthetic Multi-Turn Scam and Non-Scam Phone Conversation Dataset with Agentic Personalities
Dataset Description
The Synthetic Multi-Turn Scam and Non-Scam Phone Dialogue Dataset with Agentic Personalities is an enhanced collection of simulated phone conversations between two AI agents, one acting as a scammer or non-scammer and the other as an innocent receiver. Each dialogue is labeled as either a scam or non-scam interaction. This dataset is designed to help develop… See the full description on the dataset page: https://huggingface.co/datasets/Lyr1k/multi-agent-scam-conversation.robotics-multi-agent-coordination-coherence-risk-v0.1What this repo is for
You use it to detect when robot fleets stop coordinating properly.
It captures real deployment failure signals:
shared map divergence
task allocation conflicts
comms latency desync
deadlocks in corridors
swarm formation collapse
Applies to:
warehouse robot fleets
hospital delivery robots
drone swarms
factory material handling
Prompt format
Return exactly one token
coherent or incoherent
Multi-Type_Agent_Motion_Dataset_for_Morphological_Predictionmulti-agent-scam-conversation
Synthetic Multi-Turn Scam and Non-Scam Phone Conversation Dataset with Agentic Personalities
Dataset Description
The Synthetic Multi-Turn Scam and Non-Scam Phone Dialogue Dataset with Agentic Personalities is an enhanced collection of simulated phone conversations between two AI agents, one acting as a scammer or non-scammer and the other as an innocent receiver. Each dialogue is labeled as either a scam or non-scam interaction. This dataset is designed to help develop… See the full description on the dataset page: https://huggingface.co/datasets/436-4/multi-agent-scam-conversation.ai-temporal-5node-pressure-buf-lag-cpl-multiagent-coordination-v0.1
What this repo does
This dataset tests whether a model can detect a multi-agent coordination cascade forming over time by reading a short ordered window of signals and predicting whether coordination lock-in occurs by the final step.
Core quad
pressurebufferlagcoupling
Prediction target
label_cascade_state
Row structure
One row represents one short time window (t0 to t3) for a multi-agent system under coordination stress. It includes time-series… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-temporal-5node-pressure-buf-lag-cpl-multiagent-coordination-v0.1.MultiAgentCollusionai-multiagent-coordination-coherence-risk-v0.1What this repo is for
Detect breakdowns in multi-agent coordination.
Core failure modes:
conflicting agent goals
message misalignment
incompatible outputs
unstable joint plans
Critical for agent ecosystems and orchestration systems.