The Model Context Protocol (MCP) represents a powerful paradigm shift in how large language models interact with tools, services, and external data sources. Designed to enable dynamic tool invocation, the MCP facilitates a standardized method for describing tool metadata, allowing models to select and call functions intelligently. However, as with any emerging framework that enhances […] The post Critical Security Vulnerabilities in the Model Context Protocol (MCP): How Malicious Tools and Deceptive Contexts Exploit AI Agents appeared first on MarkTechPost. read more

Language models trained on vast internet-scale datasets have become prominent language understanding and generation tools. Their potential extends beyond language tasks to functioning as decision-making agents in interactive environments. When applied to environments requiring action choices, these models are expected to leverage their internal knowledge and reasoning to act effectively. Their ability to consider context, […] The post LLMs Struggle to Act on What They Know: Google DeepMind Researchers Use Reinforcement Learning Fine-Tuning to Bridge the Knowing-Doing Gap appeared first on MarkTechPost. read more

Recent progress in LLMs has shown their potential in performing complex reasoning tasks and effectively using external tools like search engines. Despite this, teaching models to make smart decisions about when to rely on internal knowledge versus search remains a key challenge. While simple prompt-based methods can guide models to invoke tools, LLMs still struggle […] The post Reinforcement Learning Makes LLMs Search-Savvy: Ant Group Researchers Introduce SEM to Optimize Tool Usage and Reasoning Efficiency appeared first on MarkTechPost. read more

Recent advancements in LM agents have shown promising potential for automating intricate real-world tasks. These agents typically operate by proposing and executing actions through APIs, supporting applications such as software engineering, robotics, and scientific experimentation. As these tasks become more complex, LM agent frameworks have evolved to include multiple agents, multi-step retrieval, and tailored scaffolding […] The post SWE-Bench Performance Reaches 50.8% Without Tool Use: A Case for Monolithic State-in-Context Agents appeared first on MarkTechPost. read more

In this tutorial, we demonstrate how to build a powerful and intelligent question-answering system by combining the strengths of Tavily Search API, Chroma, Google Gemini LLMs, and the LangChain framework. The pipeline leverages real-time web search using Tavily, semantic document caching with Chroma vector store, and contextual response generation through the Gemini model. These tools […] The post How to Build a Powerful and Intelligent Question-Answering System by Using Tavily Search API, Chroma, Google Gemini LLMs, and the LangChain Framework appeared first on MarkTechPost. read more

Amazon Web Services (AWS) has open-sourced its Strands Agents SDK, aiming to make the development of AI agents more accessible and adaptable across various domains. By following a model-driven approach, the Strands Agents SDK abstracts much of the complexity behind building, orchestrating, and deploying intelligent agents—making it easier for developers to build tools that plan, […] The post AWS Open-Sources Strands Agents SDK to Simplify AI Agent Development appeared first on MarkTechPost. read more

Manipulating lighting conditions in images post-capture is challenging. Traditional approaches rely on 3D graphics methods that reconstruct scene geometry and properties from multiple captures before simulating new lighting using physical illumination models. Though these techniques provide explicit control over light sources, recovering accurate 3D models from single images remains a problem that frequently results in […] The post Google Researchers Introduce LightLab: A Diffusion-Based AI Method for Physically Plausible, Fine-Grained Light Control in Single Images appeared first on MarkTechPost. read more

Conversational artificial intelligence is centered on enabling large language models (LLMs) to engage in dynamic interactions where user needs are revealed progressively. These systems are widely deployed in tools that assist with coding, writing, and research by interpreting and responding to natural language instructions. The aspiration is for these models to flexibly adjust to changing […] The post LLMs Struggle with Real Conversations: Microsoft and Salesforce Researchers Reveal a 39% Performance Drop in Multi-Turn Underspecified Tasks appeared first on MarkTechPost. read more

The growth in developing and deploying large language models (LLMs) is closely tied to architectural innovations, large-scale datasets, and hardware improvements. Models like DeepSeek-V3, GPT-4o, Claude 3.5 Sonnet, and LLaMA-3 have demonstrated how scaling enhances reasoning and dialogue capabilities. However, as their performance increases, so do computing, memory, and communication bandwidth demands, placing substantial strain […] The post This AI paper from DeepSeek-AI Explores How DeepSeek-V3 Delivers High-Performance Language Modeling by Minimizing Hardware Overhead and Maximizing Computational Efficiency appeared first on MarkTechPost. read more

In a move that signals a deeper convergence of AI and software engineering, Windsurf has announced the launch of SWE-1, its first family of AI models purpose-built for the entire software development lifecycle. Unlike traditional code generation models, SWE-1 is designed to assist with real-world software engineering workflows—handling everything from incomplete code states to multi-surface […] The post Windsurf Launches SWE-1: A Frontier AI Model Family for End-to-End Software Engineering appeared first on MarkTechPost. read more