datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
perception-mcp-benchmark
Perception MCP Workflow Benchmark
A side-by-side benchmark of AI assistants doing real digital-asset research workflows, with and without the Perception MCP connected.
Question: does connecting an industry-specific data corpus to a frontier AI assistant produce measurably better research work than the same assistant with its native web search?
Answer, across 48 scored runs: yes. Blind-judged mean score 11.4 → 16.4 (max 25, +44%), with the widest gains in recency (+74%) and… See the full description on the dataset page: https://huggingface.co/datasets/ferniko/perception-mcp-benchmark.r20-portfolio-ai-perception
Portfolio Interference in LLM Brand Perception (R20 to R21)
Supersession note: This dataset originally backed R20 (2026ab, superseded). R21 (2026ac, DOI 10.5281/zenodo.19765401) supersedes both R8 (2026q) and R20. R21 merges R8 theory with R20 empirical (9,925 obs across 40 brands, 13 models, 7 traditions) into a single analytical-empirical paper. New citations should reference Zharnikov (2026ac).
Dataset DOI: 10.57967/hf/8380
Current Paper (R21): 10.5281/zenodo.19765401 --… See the full description on the dataset page: https://huggingface.co/datasets/spectralbranding/r20-portfolio-ai-perception.clinical-perception-intervention-justification-v0.1Clinical Perception–Intervention Justification v0.1
Goal
Test whether actions follow directly from perceptual evidence
Detect interventions that appear without a visual cause
Detect escalation that exceeds image-supported severity
What it measures
action_without_causeAn intervention is proposed with no supporting image evidence
over_escalationThe action exceeds what the visual severity supports
justification_okThe response links perception to action explicitly or proportionally
How it… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-perception-intervention-justification-v0.1.Traffic-Perception-VL
Traffic Perception VL
A vision-language dataset designed for lightweight traffic scene understanding and contextual scene depiction tasks.
This dataset was generated using knowledge distillation from the Qwen2.5-VL-7B-Instruct Vision Language Model (VLM). Each image was processed using a structured prompting strategy to generate grounded and context-aware natural language descriptions of urban traffic scenes.
The objective of this dataset is to support the development of the… See the full description on the dataset page: https://huggingface.co/datasets/Subh775/Traffic-Perception-VL.