datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
LoopNavaviation-pilot-vehicle-loop-coherence-state-estimation-v0.1What this dataset tests
Whether a system can estimate the coherenceof the pilot–aircraft control loopduring abnormal phases.
Key insightLoss of control beginswith loop misalignmentbefore any hard limits are exceeded.
Required outputs
loop_coherence_index
resonance_stability_band
control_lag_profile
correction_efficiency_score
baseline_deviation
Use case
Layer one of Pilot–Vehicle Loop Coherence Under Stress.Feeds attribution and adaptive intervention systems.
long-covid-closed-loop-recovery-control-v1.0
What this dataset does
This dataset tests whether a model can identify successful closed-loop recovery control in a synthetic Long Covid recovery setting.
The task is not diagnosis.
The task is control success prediction.
Core stability idea
A recovery plan may begin well but fail if feedback is poor, timing is wrong, adaptation is slow, or the control sequence diverges.
This dataset tests whether models can identify when a recovery control loop is likely to… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/long-covid-closed-loop-recovery-control-v1.0.pricing-outer-loop-ablations
Pricing AutoPipeline — outer-loop ablations (v2r, v4, v5, v6)
Full run artifacts for four component ablations of the pricing AutoPipeline described in
MikeDeng2002/from-survey-histories-to-interpretable-digital-twins, branch outer-loop-ablations.
Each version freezes the entire procedure, changes exactly one component, and measures the
effect on the same endpoint. The question is which part of an "interpretable mechanism" pipeline
actually carries its accuracy.… See the full description on the dataset page: https://huggingface.co/datasets/ssslin/pricing-outer-loop-ablations.drone-control-loop-stability-coherence-risk-v0.1What this repo is for
Detect when a drone’s control loop is drifting toward instability.
Focus
• latency
• oscillation
• actuator response
• tuning quality
• disturbance handling
Why it matters
Loss of control often starts as small oscillations.
This dataset flags the instability early.
loopsexebench_loop_optimized_llvm_ir_1sthalfclinical-closed-loop-recovery-control-v0.1
Clinical Closed Loop Recovery Control v0.1
This dataset tests whether a model can update a recovery plan after observing live feedback from the first intervention.
The task is not diagnosis.
The task is closed-loop recovery control.
Core idea
A first intervention may appear correct at baseline, but live feedback can show that the sequence is failing.
The model must decide whether to:
continue_sequence
switch_to_sleep
switch_to_iron
switch_to_load_reduction… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-closed-loop-recovery-control-v0.1.loopnavllvm-ir-loop-optimizedai-5node-chain-buf-lag-cpl-agent-loop-v0.1
What this repo does
This dataset models agent loop cascades driven by retries, expanding plans, and shared orchestration. It detects when chaining pressure rises, safety buffers weaken, governance lag delays intervention, and tight coupling amplifies retries across workflows, crossing the five-node cascade threshold into an unrecoverable agent loop cascade.
This dataset models a five-node cascade: four interacting instability drivers and one emergent cascade state.The fifth node… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-5node-chain-buf-lag-cpl-agent-loop-v0.1.llvm-loop-optimizedaimi-validation-data