Understanding Multi-Modal Generative AI Models

Good folks at NVIDIA have just released NVLM 1.0, a family of frontier-class multimodal large language models that achieve state-of-the-art results across vision-language tasks. Here is how they did it: 1. Model Architecture Design: - Developed three model architectures: a) NVLM-D: Decoder-only architecture b) NVLM-X: Cross-attention-based architecture c) NVLM-H: Novel hybrid architecture 2. Vision Encoder: - Used InternViT-6B-448px-V1-5 as the vision encoder - Implemented dynamic high-resolution (DHR) input handling 3. Language Model: - Used Qwen2-72B-Instruct as the base LLM 4. Training Data Curation: - Carefully curated high-quality pretraining and supervised fine-tuning datasets - Included diverse task-oriented datasets for various capabilities 5. Pretraining: - Froze LLM and vision encoder - Trained only modality-alignment modules (e.g., MLP projector, cross-attention layers) - Used a large batch size of 2048 6. Supervised Fine-Tuning (SFT): - Unfroze LLM while keeping the vision encoder frozen - Trained on multimodal SFT datasets and high-quality text-only SFT data - Implemented 1-D tile tagging for dynamic high-resolution inputs 7. Evaluation: - Evaluated on multiple vision-language benchmarks - Compared performance to leading proprietary and open-source models 8. Optimization: - Iterated on model designs and training approaches - Used smaller 34B models for faster experimentation before scaling to 72B 9. Now comes the best part...Open-Sourcing: - Released model weights and full technical details to the research community The paper provides fascinating insights into architecture design, training data curation, and achieving production-grade multimodality. A must-read for anyone working on multimodal AI!

Generative AI isn’t replacing radiologists but may soon assist like a well-trained resident. 🩻 👇 A new Nature Perspective presents the frontier of Multimodal Generative AI (GenMI) in healthcare. This new class of AI models is not just interpreting medical images, it’s generating narrative reports, integrating clinical history, and even offering real-time interaction with clinicians and patients. The paper calls for a shift from single-task automation to holistic, collaborative AI assistants, or what the authors term the “AI Resident.” 👉 Key Takeaways 1. Beyond Detection: Toward Narrative Intelligence GenMI models go beyond triaging or highlighting findings,they synthesize multimodal data (e.g., imaging + clinical history) into coherent, structured reports that can rival expert drafts. 2. The “AI Resident” Paradigm Envisioned as a collaborative tool, the AI resident supports clinicians in drafting reports, enables interactive querying of findings, and can even assist in patient education and trainee feedback loops. 3. Multimodal & Multispecialty Applications While radiology is the focal domain, GenMI is expanding into pathology, dermatology, ophthalmology, and endoscopy, powered by vision-language models like GPT-4V and Google’s Gemini. 4. Challenges: Bias, Hallucination & Evaluation Gaps GenMI systems are prone to hallucinations and performance drops across underrepresented populations. Traditional NLP metrics are inadequate; new benchmarks like RadBench and RadGraph F1 are being proposed. 5. A Call for Responsible Deployment Authors advocate for gradual clinical integration, open benchmarks, diverse datasets, and human-in-the-loop calibration to ensure GenMI complements not replaces expert judgment. 🎯 GenMI represents a pivotal evolution in clinical AI from task-specific tools to interactive, multimodal assistants. If deployed with care, the AI resident could reduce burnout, democratize expertise, and reshape how medical knowledge is generated, shared, and acted upon. _________________________________________________________ #radiology #machinelearning #ai #medicine #health

Over the past year, Retrieval-Augmented Generation (RAG) has rapidly evolved—from simple pipelines to intelligent, agent-driven systems. This visual compares the four most important RAG architectures shaping modern AI design: 1. 𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚 • This is the baseline architecture. • The system embeds a user query, retrieves semantically similar chunks from a vector store, and feeds them to the LLM. • It's fast and easy to implement, but lacks refinement for ambiguous or complex queries. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Quick prototypes and static FAQ bots. 2. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗥𝗔𝗚 • A more precise and thoughtful version of Naive RAG. • It adds two key steps: query rewriting to clarify user intent, and re-ranking to improve document relevance using scoring mechanisms like cross-encoders. • This results in more accurate and context-aware responses. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Legal, healthcare, enterprise chatbots where accuracy is critical. 3. 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗥𝗔𝗚 • Designed for multimodal knowledge bases that include both text and images. • Separate embedding models handle image and text data. The query is embedded and matched against both stores. • The retrieved context (text + image) is passed to a multimodal LLM, enabling reasoning across formats. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Medical imaging, product manuals, e-commerce platforms, engineering diagrams. 4. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 • The most sophisticated approach. • It introduces reasoning through LLM-based agents that can rewrite queries, determine if additional context is needed, and choose the right retrieval strategy—whether from vector databases, APIs, or external tools. • The agent evaluates the relevance of each response and loops until a confident, complete answer is generated. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Autonomous assistants, research copilots, multi-hop reasoning tasks, real-time decision systems. As AI systems grow more complex, the method of retrieving and reasoning over knowledge defines their real-world utility. ➤ Naive RAG is foundational. ➤ Advanced RAG improves response precision. ➤ Multi-Model RAG enables cross-modal reasoning. ➤ Agentic RAG introduces autonomy, planning, and validation. Each step forward represents a leap in capability—from simple lookup systems to intelligent, self-correcting agents. What’s your perspective on this evolution? Do you see organizations moving toward agentic systems, or is advanced RAG sufficient for most enterprise use cases today? Your insights help guide the next wave of content I create.

AI apps don’t run on one model. They run on a mix, each solving a specific problem. Understanding which model does what is how you build better systems. Here’s a breakdown of key AI models powering modern applications 👇 - Language & Reasoning Models GPT, BERT, LLaMA, PaLM, Gemini, Claude handle text generation, search, chatbots, and complex reasoning tasks. - Image Generation Models Stable Diffusion, DALL·E, Midjourney create high-quality visuals from text prompts for design, media, and content. - Speech & Audio Models Whisper and DeepSpeech convert speech to text and power voice assistants and transcription tools. - Multimodal Models CLIP and Gemini connect text, images, and video - enabling search, filtering, and cross-modal understanding. - Text-to-Text & NLP Systems T5 and Transformer-based models handle translation, summarization, and structured language tasks. - Computer Vision Models YOLO, ResNet, EfficientNet, and SAM enable object detection, image classification, and segmentation in real time. - Generative Visual Models GANs generate realistic images and videos, often used in media, gaming, and simulations. - Scientific & Specialized Models AlphaFold predicts protein structures, pushing breakthroughs in drug discovery and biotech. - Core Architecture Layer Transformers power nearly all modern AI systems with attention-based learning and sequence modeling. What this means: No single model solves everything. Each one plays a role in a larger system. Strong AI products are built by combining the right models—not relying on just one. Which of these models are part of your current AI stack?

If you strip away the hype and the benchmarks, you’re left with a simple question: why do our smartest models still fail at the most basic spatial reasoning? Fei-Fei Li’s latest essay confronts this head-on - and it reframes the path forward more clearly than anything we’ve seen this year. We’ve pushed language models to astonishing heights. They can reason symbolically, generate flawless prose, and navigate complex instructions. But as someone who works with biomedical knowledge graphs, multimodal pipelines, and agentic reasoning systems, I see a recurring limitation: models can talk about the world, but they cannot think within one. This is the gap Fei-Fei Li is pointing at. Spatial intelligence - perception, geometry, causality, physical continuity, interactive reasoning - is the layer of cognition that lets humans navigate reality without narrating it. It’s the unspoken scaffolding behind everything from molecular modeling to robotics to everyday intuition. And today’s AI barely scratches it. Fei-Fei Li outlines what true world models must achieve, and the technical bar is much higher than most people realize: 1) Generative: construct worlds that remain geometrically and physically consistent over time 2) Multimodal: integrate images, video, depth, text, actions, gestures — not just tokens 3) Interactive: update the next world state when an action is applied, not just describe consequences This requires more than “bigger models.” It demands new objective functions rooted in physics and geometry, architectures that operate natively in 3D/4D space, large-scale visual and synthetic data, and memory systems that preserve continuity across time. Working on medical KG systems and reasoning agents, I’m constantly reminded of this. You can’t truly understand a biological process by reading about it - you need to model its spatial, temporal, and causal behavior. And that’s exactly what today’s text-first systems struggle with. Fei-Fei Li’s early demonstrations with Marble - generating persistent 3D environments from multimodal prompts - hint at what the next decade of AI will look like: models that don’t just describe worlds, but generate and inhabit them. Language gave us powerful narrators. World models will give us the first true actors. If we’ve spent the last decade mastering words, the next decade will belong to worlds. #SpatialIntelligence #WorldModels #AIResearch #EmbodiedAI #GenerativeAI #MultimodalAI #CognitiveArchitecture #AIFrontiers #MachinePerception #FrontierModels #DeepLearning #ArtificialIntelligence #AIInnovation #RoboticsAI #SimulationAI #3DAI #NeuralRendering #PhysicsInformedAI #FoundationModels #AGIResearch #KnowledgeGraphs #NeurosymbolicAI #AITheory #AIEcosystem #TechInnovation #FutureOfAI

A new paper from Technical University of Munich and Universitat Politècnica de Catalunya Barcelona explores the architecture of autonomous LLM agents, emphasizing that these systems are more than just large language models integrated into workflows. Here are the key insights:- 1. Agents ≠ Workflows Most current systems simply chain prompts or call tools. True agents plan, perceive, remember, and act, dynamically re-planning when challenges arise. 2. Perception Vision-language models (VLMs) and multimodal LLMs (MM-LLMs) act as the 'eyes and ears', merging images, text, and structured data to interpret environments such as GUIs or robotics spaces. 3. Reasoning Techniques like Chain-of-Thought (CoT), Tree-of-Thought (ToT), ReAct, and  Decompose, Plan in Parallel, and Merge (DPPM) allow agents to decompose tasks, reflect, and even engage in self-argumentation before taking action. 4. Memory Retrieval-Augmented Generation (RAG) supports long-term recall, while context-aware short-term memory maintains task coherence, akin to cognitive persistence, essential for genuine autonomy. 5. Execution This final step connects thought to action through multimodal control of tools, APIs, GUIs, and robotic interfaces. The takeaway? LLM agents represent cognitive architectures rather than mere chatbots. Each subsystem, perception, reasoning, memory, and action, must function together to achieve closed-loop autonomy. For those working in this field, this paper titled 'Fundamentals of Building Autonomous LLM Agents' is an interesting reading:- https://lnkd.in/dmBaXz9u #AI #AgenticAI #LLMAgents #CognitiveArchitecture #GenerativeAI #ArtificialIntelligence

🤖 LLMs vs LMMs: Understanding the Difference Artificial Intelligence has evolved rapidly — and two major families now define the frontier: Large Language Models (LLMs) and Large Multimodal Models (LMMs). While both are incredibly powerful, they differ in how they process and understand information. 📝 What Are LLMs? Large Language Models like GPT-4 and Claude are trained exclusively on text data. They excel at understanding and generating human language, performing tasks such as: ✍️ Writing and content creation 🌐 Translation across languages 📋 Text summarization 💬 Answering questions and conversations LLMs process information sequentially through tokens, using transformer architectures to understand context and relationships within text. 🎨 Enter LMMs Large Multimodal Models take AI a step further by processing multiple data types simultaneously — text, images, audio, and even video. Models like GPT-4V and Gemini can: 📸 Analyze photographs and visual content 📊 Understand complex diagrams and charts 🎥 Interpret video content 🔊 Process audio inputs 💭 Engage in text-based conversation about all of the above This multimodal capability allows them to bridge the gap between different forms of human communication. The Bottom Line While LLMs revolutionized natural language processing, LMMs represent the next frontier — systems that see, hear, and understand the world more like humans do. The choice between them depends on your needs: 🎯 Choose LLMs for: → Text processing, content writing, code generation, and language-focused tasks 🎯 Choose LMMs for: → Comprehensive multimedia understanding, visual analysis, and cross-modal applications 💬 What’s your experience with these AI models? Have you found use cases where multimodal capabilities made a significant difference? #ArtificialIntelligence #MachineLearning #LLM #AI #TechInnovation #DeepLearning #MultimodalAI

Multimodal Image Generation is finally out, and it will send ripples through the Design space. Ever since first teased with 4o almost a year ago we have been waiting for this release, and both OpenAI and Google delivered. But there is much more than Ghiblification coming. This new kind of image understanding profits from the ongoing improvements in LLM capabilities, and delivers fascinating abilities in 3D understanding and image processing. Check out my examples below, all created with really simple prompts to ChatGPT 4o or Gemini 2.0 Flash. ->Generate a Height Map from a facade photo. I got an instant 3D shape from just a picture! ->Group facade elements by color and tag them. Organizing 3D shapes, and making them easier to understand for humans and AI downstream! ->Instantly create a realistic render from a simple rhino screenshot. Super simple and precise rendering of my geometry, based on just a prompt. ->Given a building geometry screenshot, generate a detailed front view plan. It's fascinating how precise the spatial understanding of this model has become, covering fine details and spatial relationships! These initial results have been super exciting, have you tried any of these yourself? I am wondering how to best implement these advancements in Romantic Technology's Raven. Any ideas? Let me know your thoughts! #aiarchitecture #AIdesign #gpt4o #gemini

I just published a new concept paper proposing Next-Frame Generation (NFG)–a fresh take on how we train AI. This idea builds on the success of next-token generation in LLMs, but extends it to a multi-modal setting, where the model uses text to reason its way through complex sensory data (like vision or robotics controls) by “talking it out”–just like Chain-of-Thought prompting in language models. The core innovation is to leverage GRPO (Group Relative Policy Optimization)–which was IMHO the most important innovation in DeepSeek’s recent breakthroughs–in order to enhance not only text generation, but multi-modal generation. Namely, the prediction of future frames in video and multi-modal data...in a self-supervised manner. The goal: bootstrap more holistic intelligence by fusing vision, language, and potentially even sensorimotor data into a shared predictive model/latent space. I haven’t seen this exact technique used, but I’m sure it must be in the air…I’m excited to put it out there and I hope it contributes to the discussion. As an independent researcher on this work, I don’t have the lab to implement it, but I’m open sourcing the blueprint in hopes it sparks something useful for those who do. 👉 Read the full concept paper here: https://lnkd.in/gxaAZqJv 💬 Would love to hear your thoughts or critiques! #MultimodalAI #SelfSupervisedLearning #ChainOfThought #AIResearch #ReinforcementLearning #OpenScience #ArtificialIntelligence