ReVision reduces visual token usage by 46% on average in agent trajectories via a learned patch selector and improves success rates by 3% on three benchmarks, showing that history saturation stems from inefficient representations rather than lack of utility.
European Conference on Computer Vision , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
HEXST applies a hexagonal shifted-window Transformer with rotary positional encodings, contrast-sensitive training objectives, and single-cell priors to predict gene expression from histology slides, outperforming prior models on seven datasets while preserving spatial heterogeneity.
citing papers explorer
-
ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction
ReVision reduces visual token usage by 46% on average in agent trajectories via a learned patch selector and improves success rates by 3% on three benchmarks, showing that history saturation stems from inefficient representations rather than lack of utility.
-
HEXST: Hexagonal Shifted-Window Transformer for Spatial Transcriptomics Gene Expression Prediction
HEXST applies a hexagonal shifted-window Transformer with rotary positional encodings, contrast-sensitive training objectives, and single-cell priors to predict gene expression from histology slides, outperforming prior models on seven datasets while preserving spatial heterogeneity.