Large multimodal models (LMMs) enable systems to interpret images, answer visual questions, and retrieve factual information by combining multiple modalities. Their development has significantly advanced the capabilities of virtual assistants and AI systems used in real-world settings. However, even with massive training data, LMMs often overlook dynamic or evolving information, especially facts that emerge post-training […] The post This AI Paper Introduces MMSearch-R1: A Reinforcement Learning Framework for Efficient On-Demand Multimodal Search in LMMs appeared first on MarkTechPost. read more

AI has the potential to make expert medical reasoning more accessible, but current evaluations often fall short by relying on simplified, static scenarios. Real clinical practice is far more dynamic; physicians adjust their diagnostic approach step by step, asking targeted questions and interpreting new information as it comes. This iterative process helps them refine hypotheses, […] The post SDBench and MAI-DxO: Advancing Realistic, Cost-Aware Clinical Reasoning with AI appeared first on MarkTechPost. read more

What is included in this article: Performance breakthroughs – 2x faster inference and 3x faster trainingTechnical architecture – Hybrid design with convolution and attention blocksModel specifications – Three size variants (350M, 700M, 1.2B parameters)Benchmark results – Superior performance compared to similar-sized modelsDeployment optimization – Edge-focused design for various hardwareOpen-source accessibility – Apache 2.0-based licensingMarket implications […] The post Liquid AI Open-Sources LFM2: A New Generation of Edge LLMs appeared first on MarkTechPost. read more

Density Functional Theory (DFT) serves as the foundation of modern computational chemistry and materials science. However, its high computational cost severely limits its usage. Machine Learning Interatomic Potentials (MLIPs) have the potential to closely approximate DFT accuracy while significantly improving performance, reducing computation time from hours to less than a second with O(n) versus O(n³) […] The post Meta AI Introduces UMA (Universal Models for Atoms): A Family of Universal Models for Atoms appeared first on MarkTechPost. read more

Google DeepMind recently released GenAI Processors, a lightweight, open-source Python library built to simplify the orchestration of generative AI workflows—especially those involving real-time multimodal content. Launched last week, and available under an Apache‑2.0 license, this library provides a high-throughput, asynchronous stream framework for building advanced AI pipelines. Stream‑Oriented Architecture At the heart of GenAI Processors […] The post Google DeepMind Releases GenAI Processors: A Lightweight Python Library that Enables Efficient and Parallel Content Processing appeared first on MarkTechPost. read more

Kimi K2, launched by Moonshot AI in July 2025, is a purpose-built, open-source Mixture-of-Experts (MoE) model—1 trillion total parameters, with 32 billion active parameters per token. It’s trained using the custom MuonClip optimizer on 15.5 trillion tokens, achieving stable training at this unprecedented scale without the typical instabilities seen in ultra-large models. Unlike traditional chatbots, K2 is architected […] The post Moonshot AI Releases Kimi K2: A Trillion-Parameter MoE Model Focused on Long Context, Code, Reasoning, and Agentic Behavior appeared first on MarkTechPost. read more

Introduction to Embodied AI Agents Embodied AI agents are systems that exist in physical or virtual forms, such as robots, wearables, or avatars, and can interact with their surroundings. Unlike static web-based bots, these agents perceive the world and act meaningfully within it. Their embodiment enhances physical interaction, human trust, and human-like learning. Recent advances […] The post From Perception to Action: The Role of World Models in Embodied AI Systems appeared first on MarkTechPost. read more

Understanding the Link Between Body Movement and Visual Perception The study of human visual perception through egocentric views is crucial in developing intelligent systems capable of understanding & interacting with their environment. This area emphasizes how movements of the human body—ranging from locomotion to arm manipulation—shape what is seen from a first-person perspective. Understanding this […] The post This AI Paper Introduces PEVA: A Whole-Body Conditioned Diffusion Model for Predicting Egocentric Video from Human Motion appeared first on MarkTechPost. read more

Mistral AI, in collaboration with All Hands AI, has released updated versions of its developer-focused large language models under the Devstral 2507 label. The release includes two models—Devstral Small 1.1 and Devstral Medium 2507—designed to support agent-based code reasoning, program synthesis, and structured task execution across large software repositories. These models are optimized for performance […] The post Mistral AI Releases Devstral 2507 for Code-Centric Language Modeling appeared first on MarkTechPost. read more

Developers are actively working to bring AI agents to market, but a significant hurdle has been the lack of memory. Without the ability to recall past interactions, agents treat each conversation as if it’s the first, leading to repetitive questions, an inability to remember user preferences, and a general lack of personalization. This results in […] The post Google AI Releases Vertex AI Memory Bank: Enabling Persistent Agent Conversations appeared first on MarkTechPost. read more