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Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents

Hugging Face Blog · huggingface.co · March 31, 2026
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#artificial intelligence Open access Feb 2025

Iterative Flow Matching - Path Correction and Gradual Refinement for Enhanced Generative Modeling

This work explores image generation using flow matching using flow matching and proposes an iterative process that can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.

Eldad Haber, Shadab Ahamed, Md Shahriar Rahim Siddiqui et al. · 3 citations

EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding

EgoMemReason is introduced, a comprehensive benchmark for week-long egocentric video understanding through memory-driven reasoning that evaluates three complementary memory types: entity memory, tracking how object states evolve and change across days; event memory, recalling and ordering activities separated by hours or days; and behavior memory, abstracting recurring patterns from sparse, repeated observations over the whole week period.

Ziyang Wang, Yue Zhang, Shoubin Yu et al. · 2 citations
#artificial intelligence Preprint Jul 2026

Mask2Real-WM: Segmentation Masks as a Sim-to-Real Bridge for Controllable Dexterous World Models

Mask2Real-WM is presented, a two-stage action-conditioned world model for dexterous manipulation that decouples pixel prediction into a dynamics model and a rendering model that shows that mask conditioning and simulation pretraining are both required for per-DoF action controllability across all 23 degrees of freedom.

Riccardo Feingold, Davide Liconti, Chenyu Yang et al. · 1 citation

Planning-aligned Token Compression for Long-Context Autonomous Driving

This work proposes COMPACT-VA, a planning-aligned working memory framework built on conditional VQ-VAE, compressing extended context into bounded representations, and evaluates on high-signal dynamic scenarios where historical context is most critical for behavior correctness, and accordingly design behavioral metrics.

Zhixuan Liang, Yuxiao Chen, Yurong You et al. · 1 citation