vision-language-model

OpenBMB thanks @_akhaliq for contributing a Hugging Face demo for MiniCPM-V 4.6, using Gradio server for flexible frontend customization.

Numind released NuExtract3, a 4B open-weight vision-language model based on Qwen3.5-4B, designed for converting document images to Markdown, OCR, and structured data extraction. It is Apache-2.0 licensed and self-hostable with quantized versions for low VRAM.

This paper investigates using vision-language models to assess nursing competency from egocentric video during simulation, finding that recognition accuracy inversely relates to competency level, suggesting a pedagogically informative signal.

University of Florida Gators submission to the AmericasNLP 2026 shared task on cultural image captioning for Indigenous languages, using a two-stage pipeline with Qwen2.5-VL for Spanish captioning and retrieval-augmented Gemini 2.5 Flash for target-language translation, achieving significant improvements over the baseline.

SimGym is a framework that simulates A/B tests on e-commerce storefronts using vision-language model agents, reducing experimental cycles from weeks to under an hour while achieving 77% directional alignment with real buyer behavior.

AutoRubric-T2I automatically generates and selects explicit rubrics to guide Vision-Language Model judges for text-to-image generation, achieving high-quality reward signals with minimal human annotation and improving generation quality in downstream tasks.

Aurora is an agentic video editing framework that pairs a tool-augmented vision-language model agent with a diffusion transformer to automatically resolve textual and visual underspecification in user requests, enabling unified video editing tasks like replacement, removal, style transfer, and reference-driven insertion.

This paper introduces PAGER, a topology-aware agent that bridges the semantic-execution gap in point-precise GUI control, achieving 4.1x higher task success than baselines on the new PAGE Bench.

RoboEvolve is a framework that co-evolves a VLM planner and VGM simulator for robotic manipulation, achieving data efficiency with only 500 unlabeled seed images and robust continual learning.

The author explores whether language models can create art through an iterative painting process rather than one-shot generation, building an app that uses a vision-language model to apply strokes one at a time. The experiment highlights the fragility of LLM-generated artefacts and reflects on artistic sincerity.

MiniCPM-V 4.6 is an ultra-efficient 1.3B vision-language model optimized for mobile devices.

OpenBMB has launched the MiniCPM-V 4.6 vision language model, which features immediate day-0 support on the MLX-VLM package for high-speed inference on Apple Silicon Macs.

RoboMemArena introduces a large-scale benchmark for evaluating robotic memory across 26 complex tasks with real-world validation, alongside PrediMem, a dual-system vision-language-action model that improves memory management through predictive coding.

docext is an on-premises toolkit that converts images and PDFs to markdown without OCR, leveraging vision-language models. It also introduces Nanonets-OCR-s, a compact 3B parameter model for efficient image-to-markdown conversion.

MolmoAct 2 is an open robotics model that reasons in 3D space before taking actions, developed by the Allen Institute for Artificial Intelligence.

dots.ocr is a new lightweight 1.7B parameter multilingual vision-language model that achieves state-of-the-art performance on OmniDocBench, outperforming much larger models (72B+) at document parsing and OCR tasks.

SGOCR is an open-source dataset pipeline for generating spatially-grounded, OCR-focused visual question answering (VQA) tuples with rich metadata to support diverse VLM training. The pipeline uses a multi-stage approach combining models like Nvidia's nemotron-ocr-v2, Gemma4, Qwen3-VL, and Gemini-2.5-Flash, along with an agentic optimization loop.

IBM releases Granite 4.0 3B Vision, a compact vision-language model designed for enterprise document understanding, featuring specialized capabilities for table extraction, chart interpretation via ChartNet, and key-value pair grounding.

This paper presents dots.ocr, a unified Vision-Language Model that jointly learns layout detection, text recognition, and relational understanding for multilingual document layout parsing. It achieves state-of-the-art results on OmniDocBench and introduces the XDocParse benchmark spanning 126 languages.

Hulu-Med is a transparent medical vision-language model that unifies understanding across text, 2D/3D images, and video, achieving state-of-the-art performance on 30 benchmarks while being fully open-source.