datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
robotics-world-model-coherence-drift-v0.1What this repo is for
You use it to detect when a robot’s internal world model no longer matches reality.
It captures failures like:
map outdated after environment change
localisation drift
object position mismatch
robot navigating into blocked space
Applies to:
warehouse robots
delivery robots
drones
inspection robots
industrial manipulators
Prompt format
Return exactly one token
coherent or incoherent
image-editing-model-notes
Image Editing Model Notes
Working notes on image-generation and prompt-based editing models.
I’m mainly interested in what happens after the first good-looking image:
whether the model follows small editing instructions
whether faces and expressions remain consistent
whether untouched objects quietly change
how well models handle text replacement
whether exact object counts are respected
how lighting edits affect skin and image texture
A visually strong result is not always a… See the full description on the dataset page: https://huggingface.co/datasets/drifterAI3000/image-editing-model-notes.ai-latent-cross-coupling-model-drift-v0.1
What this repo does
This repository introduces a Clarus dataset for detecting latent instability under cross-coupled conditions in AI systems.
The goal is to identify models or deployed inference systems that may still appear outwardly stable or only mildly anomalous but already contain hidden internal degradation that may activate into overt model drift once interacting pressures exceed containment.
Core structure
This dataset models a pre-failure geometry built from:… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-latent-cross-coupling-model-drift-v0.1.