H Company’s new Holo3 series delivers a visual language model that outperforms GPT‑5.4 on the OSWorld‑Verified benchmark with a 78.85% score while costing only about one‑tenth as much, offering both a flagship API‑only version and an open‑source lightweight variant optimized for GUI agents.
This article explores the LabClaw skill library, LabOS laboratory operating system, and MedOS surgical platform—detailing their modular AI capabilities, multi‑agent architectures, benchmark results, and how they together create a self‑evolving ecosystem that transforms AI into a real‑time collaborative scientist for biomedical research and clinical practice.
This article introduces PaperBanana, a multi‑agent AI framework that automates the creation of academic illustration by retrieving references, planning descriptions, styling, visualizing, and iteratively refining images, and evaluates its performance on the new PaperBananaBench benchmark against existing baselines.
The Kimi K2.5 technical report reveals how a Chinese team combined joint text‑vision training, a novel Zero‑Vision SFT method, and a parallel agent‑swarm architecture to deliver top‑ranked multimodal performance, dramatically faster inference, and open‑source access for broader AI research.
FantasyHSI introduces a graph‑based multi‑agent framework that combines visual‑language models and video‑generation diffusion to let digital humans perceive, plan, and interact autonomously in any 3D scene, producing physically plausible, long‑duration actions for animation creation and embodied‑AI simulation.
FSDrive introduces a spatio‑temporal chain‑of‑thought approach that enables visual language models to generate future driving scenes as images, improving trajectory planning accuracy and safety by eliminating cross‑modal gaps and enforcing physical constraints in autonomous driving.
The POINTS-Reader paper, accepted at EMNLP 2025, introduces a two‑stage, fully automated data generation pipeline that enables a lightweight visual‑language model to extract text, tables, and LaTeX formulas from diverse PDF layouts with superior performance and high throughput, all without relying on costly teacher‑model distillation.
The ERNIE‑4.5‑VL visual‑language model breaks single‑modality limits by delivering breakthrough image, video, and text understanding across more than 100 languages, offering lightweight yet competitive performance against models like Qwen2.5‑VL, supporting 128K context, dual “thinking” modes, and extensive deployment resources.
dots.ocr is a 1.7 billion-parameter multilingual document-parsing model that unifies layout detection and content recognition within a single visual-language model, delivering state-of-the-art performance across text, tables, formulas and reading order while remaining efficient and extensible for future multimodal AI research.
This article presents a comprehensive overview of multimodal Retrieval‑Augmented Generation (RAG), detailing three implementation paths—semantic extraction, Transformer‑based, and Visual Language Model approaches—along with scaling strategies using tensor indexing, performance comparisons, and guidance on selecting the most suitable technical route.
This article presents a step‑by‑step agentic object detection pipeline that combines open‑vocabulary detectors such as Grounding‑DINO with visual language models (GPT‑4o, o1) for concept extraction, critique, refinement, and validation, complete with code snippets, design rationale, and real‑world examples.
Zhipu AI unveiled the GLM-4.1V-Thinking series, an open‑source multimodal model that outperforms larger rivals on visual‑language tasks, supports video analysis, GUI agents, and advanced scientific reasoning, while introducing a curriculum‑sampling reinforcement‑learning framework and a new Agent application platform.
SuperEdit introduces rectified instruction generation and contrastive supervision to fix noisy training signals in instruction‑based image editing, achieving up to 9.19% performance gains without extra parameters or pre‑training, as demonstrated on the Real‑Edit benchmark.
This report details a comprehensive set of optimizations for multimodal visual large‑model (VLM) inference—including image pre‑processing acceleration, TensorRT integration for the ViT module, CUDA‑Graph replay, token‑count reduction, prefix‑cache handling, and weight quantization—demonstrating up to three‑fold throughput gains while maintaining accuracy.
Alibaba's newly released Qwen2.5‑VL‑32B multimodal model delivers state‑of‑the‑art visual and textual performance, offering human‑aligned responses, superior mathematical reasoning, fine‑grained image understanding, and efficient deployment features that make it a compelling tool for developers and AI researchers alike.
ImageRAG introduces a retrieval‑augmented generation framework that dynamically fetches relevant images to guide diffusion models, dramatically improving the synthesis of rare and fine‑grained concepts across multiple text‑to‑image systems, as demonstrated by extensive quantitative and user studies.
This article explores multimodal Retrieval‑Augmented Generation (RAG), detailing five core topics—including semantic extraction, visual‑language models, scaling strategies, technical roadmap choices, and a Q&A—while presenting three implementation pathways, performance evaluations, and future directions for AI‑driven document understanding.
ImageRAG introduces a dynamic retrieval‑augmented generation framework that integrates visual language models and CLIP‑based similarity search to supply reference images, enabling diffusion models like OmniGen and SDXL to better render rare and fine‑grained concepts, as demonstrated through extensive quantitative and qualitative experiments.
The talk outlines Multimodal RAG implementation routes, comparing OCR‑based object recognition, transformer encoder‑decoder encoding, and Visual Language Model processing, explains the ColPali late‑interaction method for multi‑dimensional vector matching, addresses scaling tensors with binarization and reranking, and recommends a hybrid long‑term strategy where VLM excels on abstract imagery while traditional OCR remains valuable.
This article examines the implementation paths and future prospects of multimodal Retrieval‑Augmented Generation, covering semantic extraction, transformer‑based OCR, visual language models, scaling challenges, tensor indexing, and practical evaluations with tools like Infinity and ColPali.
Vitron is a unified pixel‑level visual multimodal large language model that integrates image, video, and region encoders with a text‑centric strategy, delivering precise pixel‑wise perception and a comprehensive suite of vision tasks from understanding to generation and editing.
Google’s new ScreenAI visual‑language model, built on the PaLI architecture, can interpret user interfaces and infographics, answer UI‑related questions, generate summaries and navigate screens, and sets new benchmarks that may reshape future user‑experience research and applications.
This article introduces LLaVA, an open‑source large language‑visual assistant that replicates GPT‑4‑V capabilities, explains its architecture, training process, and key features, and provides step‑by‑step instructions for using the web demo, running it locally via Ollama or HuggingFace, and building a simple Gradio chatbot with code examples.
