datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ahsanneural_drone-swarm-coordination-dataset
Drone Swarm Coordination
Simulated multi-drone swarm flight paths with communication and collision log
Dataset Info
Source: Kaggle
Original Size: 1.11 MB
Kaggle Downloads: 47
Files: 1
Files
synthetic_drone_swarm_dataset.csv
Mirrored from Kaggle
coordination-fragility-detection-v0.1
What this dataset does
This dataset tests whether a model can detect coordination fragility.
The task is simple:
Given a scenario and a coordination-fragility claim, predict whether the claim is supported.
Core stability idea
Some systems fail because individual components are weak.
Other systems fail because coordination requirements are too strict.
Coordination fragility occurs when:
success requires perfect synchronization
timing tolerance is low
dependencies are… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/coordination-fragility-detection-v0.1.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
atc-handoff-coordination-coherence-risk-v0.1What this repo is for
Detect when inter-sector coordination starts to fail.
Signals:
late handoffs
readback errors
coordination call load
boundary conflicts
rising delays
This flags brittleness before incidents.
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.ai-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.