# multimodal-ai A machine-learning approach that processes and integrates multiple data modalities, such as text, images, audio, and video. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## MindTopo reveals VLMs' spatial reasoning abilities DevFeed: [MindTopo reveals VLMs' spatial reasoning abilities]() Original publisher: [Read original article]() Author: Yunfei Ge, Anbang Liu, Qineng Wang, Johnalbert Garnica, Zihan Wang, Reuben Tan, Jianfeng Gao, Ruohan Zhang, Yining Hong, Jiajun Wu, Manling Li Published: 2026-08-12T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [Benchmark](), [LLM evaluation / benchmarking](), [multimodal-ai](), [Language models](), [Robotics]() Tags: [ai](), [benchmark](), [large-language-models](), [multimodal](), [reasoning](), [research](), [research-blog](), [testing](), [vlms]() ### AI overview MindTopo is a benchmark for evaluating whether multimodal large language models can understand and manipulate topological relationships such as connectivity, enclosure, order, separation, and knots. It compares static recognition with interactive planning and finds that current models often lose track of structural relationships during sequences of actions. ### Source excerpt A path, a fence, a knot. MindTopo sets a new benchmark for testing how AI understands topological relationships and highlights new opportunities to strengthen spatial reasoning and planning. The post MindTopo reveals VLMs' spatial reasoning abilities appeared first on Microsoft Research. ## Building a Local, Multimodal AI Terminal Agent with Gemma 4 DevFeed: [Building a Local, Multimodal AI Terminal Agent with Gemma 4]() Original publisher: [Read original article]() Author: Arjun Prabhulal Published: 2026-08-12T09:25:11Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [gemma4](), [Artificial Intelligence](), [multimodal-ai](), [Command-line interface](), [Large Language Model](), [Local AI](), [Code](), [Google]() Tags: [ai](), [ai-agent](), [artificial-intelligence](), [code](), [function-calling](), [gemma](), [gemma-4](), [google](), [google-cloud-platform](), [local-ai](), [multimodal](), [ollama](), [open-source](), [terminal]() ### AI overview A tutorial introduces Gemma 4 and builds a local multimodal terminal agent named gemma4-agent. It covers function calling, tool orchestration, text, image, and voice processing, plus Gemma 4's model variants and architecture. ### Source excerpt Introduction Open-source LLM models have been improving rapidly with tool calling, extended context windows, and native vision and audio capabilities, all while delivering strong benchmark performance. Gemma 4, recently introduced by Google Deepmind brings all of these features together in sizes efficient enough to run locally. In this article, we'll look at the capabilities of Gemma 4 and build a multimodal (Text, Vision, Voice) CLI agent (gemma4-agent) with function-calling capabilities. By the end, you'll have an agent that can chat, write, execute code, analyze images, and process voice instructions to deliver highly grounded responses. What is Gemma 4 Model ? Gemma 4 is Google DeepMind's open model family, released in April 2026 under the Apache 2.0 license. Built from the same research and technology behind Gemini 3, Gemma 4 is designed for high-performance reasoning, coding, multimodal understanding, and local AI execution across different model sizes. Features of Gemma 4 Models Improved Tool calling : Native function calling and tool orchestration, letting agents act autonomously without bloating prompt instructions Thinking mode : Built-in step-by-step thinking mode via the <|think|> token for complex multi-turn logic Context Windows : Up to 256K tokens on the 12B and larger models (128K on the edge-sized E2B/E4B) for processing long document and tool outputs Extended Multimodality : Gemma 4 models can process text,voice and images simultaneously like extracting data from charts, analyzing screenshots , and reviewing UI mockups. Gemma 4 Model Variants & SpecificationsGemma 4 Architecture Gemma 4 comes in five model sizes built around four architectural variants, each making different trade-offs between performance, inference speed, compute, and memory. Gemma4 Unified 12B vs Effective Parameters Effective-parameter models (E2B and E4B) are dense transformer models optimized for edge and on-device deployment. The "E" stands for effective parameters use Per-La ## BIY: Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks DevFeed: [BIY: Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks]() Original publisher: [Read original article]() Author: João Palmeiro Published: 2026-01-19T14:52:55Z Content type: article Language: en Sources: [Feedzai]() Topics: [Accessibility](), [LLM evaluation / benchmarking](), [multimodal-ai](), [Canvas](), [AI Models](), [dataset](), [SVG]() Tags: [accessibility](), [ai-models](), [artificial-intelligence](), [benchmarking](), [canvas](), [clustering](), [data-visualization](), [dataset](), [large-language-models](), [multimodal](), [multimodal-ai](), [outlier-detection](), [svg]() ### AI overview The article introduces Benchmark It Yourself (BIY), an effort to prepare a dataset and benchmark multimodal AI models for scatterplot-related tasks. It examines generating distribution-aware descriptions from scatterplot images to improve the accessibility of canvas charts, including direct description generation and structured-data extraction for predefined templates. Initial results were mixed. ### Source excerpt Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks When we need to visualize and interact with millions, or even just thousands, of individual points while analyzing data, we typically resort to rendering them in the browser using a canvas. The other common approach for the web, SVG, doesn't scale when the number of individual elements increases to such quantities. However, while solving one problem, canvas charts introduce a new challenge: accessibility. Although SVG charts are not accessible by default, they can be by design. Each part of an SVG chart has a corresponding element on the web page, allowing for a programmable, accessible experience for screen reader users. We can simply think of SVG as HTML. On the other hand, a canvas chart is just like a PNG image. If a screen reader user tries to learn more about a canvas chart, unless the developer has prepared a detailed description of it, they will just hear the word "image". There's no way to get an idea of what one of these charts represents, let alone extract any insights. For static charts, the solution can be as simple as preparing a description and integrating it into the rendered chart. However, for platforms leveraging dynamic, large datasets, automatically generating these descriptions is not a simple task, especially for charts like scatterplots where data distributions can assume countless forms. At Feedzai, we started exploring ways to generate data distribution-aware descriptions for scatterplots from their respective images using recent multimodal AI models. When the raw data is not available, or the datasets are composed of several thousand or million instances, relying on chart images and these models becomes tempting. This combination has the potential to generate such descriptions and serve them alongside their respective charts, significantly improving the accessibility of canvas charts. That said, we focused on two main directions: using ## StreetReaderAI: Towards making street view accessible via context-aware multimodal AI DevFeed: [StreetReaderAI: Towards making street view accessible via context-aware multimodal AI]() Original publisher: [Read original article]() Published: 2025-10-29T16:38:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Accessibility](), [multimodal-ai](), [Artificial Intelligence](), [Google](), [Image](), [alt text](), [real-time]() Tags: [accessibility](), [ai](), [alt-text](), [audio](), [gaming](), [generative-ai](), [human-computer-interaction-and-visualization](), [machine-intelligence](), [multimodal](), [multimodal-ai](), [natural-language-processing](), [navigation](), [real-time](), [research](), [screen](), [vision](), [voice]() ### AI overview Google Research introduces StreetReaderAI, a proof-of-concept accessible street view prototype that uses context-aware, real-time multimodal AI to interpret streetscape imagery. Designed with blind and sighted accessibility researchers, it provides audio-led exploration, keyboard and voice navigation, directional feedback, movement guidance, and descriptions of nearby places and geographic information. ### Source excerpt Generative AI ## Image and audio models from fal now available on DigitalOcean DevFeed: [Image and audio models from fal now available on DigitalOcean]() Original publisher: [Read original article]() Author: Grace Morgan Published: 2025-10-23T12:30:00Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [AI Platform](), [Digital Ocean](), [multimodal-ai](), [API](), [stable-diffusion](), [flux](), [Serverless](), [voice ai]() Tags: [ai-platform](), [api](), [audio](), [digitalocean](), [flux](), [multimodal](), [multimodal-ai](), [product-updates](), [serverless](), [stable-diffusion]() ### AI overview DigitalOcean announces four multimodal AI models from fal in public preview on the Gradient AI Platform through Serverless Inference. The models support image generation, audio generation, and multilingual text-to-speech through an API. ### Source excerpt We're excited to announce the launch of four multimodal AI models from fal on the DigitalOcean Gradient™ AI Platform, now available in public preview through Serverless Inference. These models allow you to generate images and audio directly via API, without worrying about infrastructure, scaling, or vendor management. With this release, building AI-powered applications that include visual and audio content is easier than ever. Explore the new models The fal models, now in public preview, cover a variety of modalities, enabling you to experiment, prototype, and deploy multimodal AI features quickly: Image generation: Stable Diffusion XL fast (fal-ai/fast-sdxl) - High-resolution image generation FLUX.1 (schnell) (fal-ai/flux/schnell) - Fast image generation for quick prototyping Audio generation: Stable Audio (fal-ai/stable-audio-25/text-to-audio) - Convert text into natural-sounding audio ElevenLabs TTS Multilingual v2 9 (fal-ai/elevenlabs/tts/multilingual-v2) - Multilingual text-to-speech These models are available via Serverless Inference, letting you generate images and audio through the same simple API-driven workflow you already use on Gradient AI Platform. Try it out You can start using these models through the Serverless Inference API (https://inference.do-ai.run) after opting in to the public preview in the DigitalOcean console. Here's a quick look at how to interact with them: First, opt in to the public preview to access the fal models on the Gradient AI Platform. Once opting in, it should take about 10 to 15 minutes for your access to be granted. Example: Generate an Image export MODEL_ACCESS_KEY="YOUR_KEY" curl -sS -X POST 'https://inference.do-ai.run/v1/async-invoke' \ -H "Authorization: Bearer $MODEL_ACCESS_KEY" \ -H "Content-Type: application/json" \ -d '{ "model_id": "fal-ai/flux/schnell", "input": { "prompt": "A high-quality photo of a futuristic city at sunset" } }' Example: Generate an Image with Customized Parameters export MODEL_ACCESS_KEY="YOUR_ ## TimeScope: How Long Can Your Video Large Multimodal Model Go? DevFeed: [TimeScope: How Long Can Your Video Large Multimodal Model Go?]() Original publisher: [Read original article]() Author: Orr Zohar; Rui Li; Andres Marafioti; Xiaohan Wang Published: 2025-07-23T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Benchmark](), [vlm](), [long-context](), [multimodal-ai](), [hugging face]() Tags: [artificial-intelligence](), [benchmark](), [datasets](), [hugging-face](), [language-models](), [long-context](), [measurement](), [multimodal](), [multimodal-ai](), [open-source](), [reasoning](), [retrieval](), [video]() ### AI overview TimeScope is an open-source benchmark for evaluating how vision-language models understand long videos. It inserts short video clips into videos ranging from one minute to eight hours and measures localized retrieval, information synthesis, and fine-grained temporal perception. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Visual Salamandra: Pushing the Boundaries of Multimodal Understanding DevFeed: [Visual Salamandra: Pushing the Boundaries of Multimodal Understanding]() Original publisher: [Read original article]() Author: Iñigo Pikabea; Jaume Lozano Published: 2025-04-11T14:21:56Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [multimodal-ai](), [vlm](), [Artificial Intelligence](), [Large Language Model](), [datasets](), [Embeddings](), [mlp](), [AI Research]() Tags: [ai](), [blog](), [blog-post](), [datasets](), [embeddings](), [grounding](), [hugging-face](), [language](), [llm](), [mlp](), [models](), [multimodal](), [multimodal-ai](), [ocr](), [research](), [training](), [vision](), [vqa]() ### AI overview Visual Salamandra is a multilingual multimodal model built by extending the Salamandra Instructed 7B model with Google's SigLIP image encoder, an MLP projector, and late-fusion techniques. It processes text, images, and videos, with training focused on visual grounding, document understanding, mathematical reasoning, OCR, and European-language coverage. ### Source excerpt A Blog post by Language Technologies Laboratory @ Barcelona Supercomputing Center on Hugging Face ## SmolVLM - small yet mighty Vision Language Model DevFeed: [SmolVLM - small yet mighty Vision Language Model]() Original publisher: [Read original article]() Author: Andres Marafioti; merve; Miquel Farré; Elie Bakouch; Pedro Cuenca Published: 2024-11-26T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [smolvlm](), [vlm](), [multimodal-ai](), [Transformers](), [Fine-tuning](), [Inference](), [Open Source Models & Datasets](), [GPU]() Tags: [apache](), [fine-tuning](), [gpu](), [inference](), [llm](), [multimodal](), [multimodal-ai](), [nlp](), [on-device](), [open](), [open-source](), [smolvlm](), [transformers](), [trl](), [vision]() ### AI overview This article introduces SmolVLM, a family of small, fast, memory-efficient 2B vision-language models released fully open source under the Apache 2.0 license. It describes the model variants, architecture, training resources, Transformers integration, demo, fine-tuning script, and efficient local or on-device deployment. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.