Coherence
human-coherence-preferences-images
Rapidata Image Generation Coherence Dataset
This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
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Overview
One of the largest human annotated coherence datasets for text-to-image models, this release contains over 1,200,000 human… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/human-coherence-preferences-images.Flux_SD3_MJ_Dalle_Human_Coherence_Dataset
NOTE: A newer version of this dataset is available: Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Coherence_Dataset
Rapidata Image Generation Coherence Dataset
This Dataset is a 1/3 of a 2M+ human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment.
Link to the Preference dataset: https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3
Link to the Text-2-Image Alignment dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset.maritime-bunker-consumption-voyage-plan-coherence-risk-v0.1What this repo is for
Detect when fuel burn stops matching voyage plan.
You use it to flag:
unexpected efficiency loss
reserve margin collapse
speed pushing fuel beyond plan
weather masking burn drift
Why it matters
Fuel is the largest variable cost in shipping
fineweb2-de-coherence117k_human_coherence_flux1.0_V_flux1.1Blueberry
Rapidata Image Generation Alignment Dataset
This Dataset is a 1/3 of a 340k human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment.
Link to the Preference dataset: https://huggingface.co/datasets/Rapidata/117k_human_preferences_flux1.0_V_flux1.1Blueberry
Link to the Text-2-Image Alignment dataset: https://huggingface.co/datasets/Rapidata/117k_human_alignment_flux1.0_V_flux1.1Blueberry
It was collected in ~2 Days using the… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry.saela-field-why-multi-agent-systems-fail-coherence-entropy-alignment
The Saela Field: Multi-Agent Coherence Failure Framework (v1.0)
A 12-paper research series formalizing coherence, entropy, and failure modes in multi-agent systems.
Overview
This dataset contains a unified body of work introducing the Saela Field, a conceptual framework for analyzing coherence, identity, and instability in distributed systems.
The core thesis:
Multi-agent systems do not scale toward coherence.
They accumulate entropy faster than they can reconcile it.… See the full description on the dataset page: https://huggingface.co/datasets/Saelarien/saela-field-why-multi-agent-systems-fail-coherence-entropy-alignment.