mllms
Progress from zero to frontier with a guided depth ladder.
What is an MLLM? (Multimodal LLM)
MLLMs explained: models that understand and generate across text, images, audio, and more.
MLLMs for Document Understanding — A Practical Playbook
How to use multimodal LLMs for invoices, contracts, reports, and forms with accuracy and traceability.
MLLMs in Practice: What Vision-Language Models Can and Cannot Do in 2026
Multimodal large language models can now see, hear, and read. Here's what they're actually good at in 2026, where they still fall short, and how to use them in real workflows.
Vision-Language Models: How MLLMs Understand Images and Text Together
A technical deep dive into multimodal large language models (MLLMs) — how vision encoders connect to language models, what architectural choices matter, and how capability limits manifest in practice.
Audio-Visual Multimodal Models: How They Work and What They Can Do
The next frontier for MLLMs isn't just text + images — it's audio and video. Here's how audio-visual models work and what capabilities they enable.
Benchmarking Multimodal LLMs: What to Measure and How
A practical guide to evaluating multimodal LLMs — from standard benchmarks to building your own evaluation suite.
MLLMs for Chart and Data Understanding: Reading Graphs Like a Human
Multimodal LLMs can now read charts, extract data from graphs, and answer questions about visualizations. Here's how well they actually work, where they fail, and how to use them effectively.
MLLMs for Code and Visual Reasoning: When Models Read Diagrams, Screenshots, and Whiteboards
Multimodal LLMs can now look at a screenshot, diagram, or whiteboard sketch and generate working code or structured analysis. Here's what works, what doesn't, and how to build with it.
MLLMs for Grounded UI Agents: Why Vision-Language Models Matter
How multimodal language models enable grounded UI agents by connecting screenshots, layout understanding, and action planning.
Visual Grounding and Reasoning in Multimodal LLMs
How MLLMs understand the spatial structure of images, locate specific objects, and reason about visual relationships — the technical foundations of grounding and visual reasoning.
MLLMs for Medical Imaging: Current Capabilities and Limits
Multimodal large language models are entering medical imaging workflows, but the gap between demo and deployment is wide. Here's where they actually work, where they fail, and what responsible adoption looks like.
Multimodal LLM Safety: Alignment Challenges Across Modalities
An exploration of the unique safety and alignment challenges that arise when LLMs process images, audio, and video — covering cross-modal attacks, evaluation gaps, and defense strategies.
MLLMs for OCR and Document AI: Beyond Traditional Text Recognition
Multimodal LLMs are replacing traditional OCR pipelines for document understanding. They read layouts, understand context, and extract structured data from messy real-world documents.
MLLMs in the Wild: Real-World Visual Understanding Beyond Benchmarks
How multimodal large language models perform on real-world visual understanding tasks — the gaps between benchmark scores and production accuracy, failure modes, and practical mitigation strategies.
Spatial Understanding in Multimodal LLMs: How Models Reason About Space
Modern MLLMs can describe what's in an image but often struggle with where things are. This guide explores spatial reasoning capabilities, limitations, and techniques for improvement.
Tool Use and Function Calling in Multimodal LLMs
Multimodal LLMs can now see an image and decide to call an API based on what's in it. This guide covers how tool use works in MLLMs, architectural patterns, and practical implementation.
MLLMs for UI Understanding
Multimodal models are getting surprisingly good at reading interfaces. Here's how UI understanding works, where it breaks, and why it matters for computer-use systems.
MLLMs and Video Understanding: What's Now Possible
Multimodal large language models can now process video — understanding scenes, tracking events across time, and extracting structured information from moving images. Here's what's production-ready and what isn't.
MLLMs for Robotics and Embodied AI
How multimodal large language models are reshaping robotics—from vision-language-action models and embodied reasoning to real-world manipulation, navigation, and the challenges of bridging digital intelligence with physical action.