TLDR: VISTA is a multi agent framework that improves text to video generation during inference, it plans structured prompts as scenes, runs a pairwise tournament to select the best candidate, uses specialized judges across visual, audio, and context, then rewrites the prompt with a Deep Thinking Prompting Agent, the method shows consistent gains over strong […]

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OpenAI just launched ChatGPT Atlas, a new AI browser that embeds ChatGPT at the core of navigation, search, and on-page assistance. Atlas is available today for Free, Plus, Pro, and Go users, with a Business beta and Enterprise/Edu opt-in; Windows, iOS, and Android builds are “coming soon.” What ChatGPT Atlas is? Atlas is a Chromium-based […]

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In this tutorial, we dive into the essence of Agentic AI by uniting LangChain, AutoGen, and Hugging Face into a single, fully functional framework that runs without paid APIs. We begin by setting up a lightweight open-source pipeline and then progress through structured reasoning, multi-step workflows, and collaborative agent interactions. As we move from LangChain […]

The post How I Built an Intelligent Multi-Agent Systems with AutoGen, LangChain, and Hugging Face to Demonstrate Practical Agentic AI Workflows appeared first on MarkTechPost.

A team of researchers from Google Research and UC Santa Cruz released DeepSomatic, an AI model that identifies cancer cell genetic variants. In research with Children’s Mercy, it found 10 variants in pediatric leukemia cells missed by other tools. DeepSomatic has a somatic small variant caller for cancer genomes that works across Illumina short reads, […]

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DeepSeek-AI released 3B DeepSeek-OCR, an end to end OCR and document parsing Vision-Language Model (VLM) system that compresses long text into a small set of vision tokens, then decodes those tokens with a language model. The method is simple, images carry compact representations of text, which reduces sequence length for the decoder. The research team […]

The post DeepSeek Just Released a 3B OCR Model: A 3B VLM Designed for High-Performance OCR and Structured Document Conversion appeared first on MarkTechPost.

The landscape of AI is expanding. Today, many of the most powerful LLMs (large language models) reside primarily in the cloud, offering incredible capabilities but also concerns about privacy and limitations around how many files you can upload or how long they stay loaded. Now, a powerful new paradigm is emerging. This is the dawn […]

The post The Local AI Revolution: Expanding Generative AI with GPT-OSS-20B and the NVIDIA RTX AI PC appeared first on MarkTechPost.

While a basic Large Language Model (LLM) agent—one that repeatedly calls external tools—is easy to create, these agents often struggle with long and complex tasks because they lack the ability to plan ahead and manage their work over time. They can be considered “shallow” in their execution. The deepagents library is designed to overcome this […]

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Anthropic recently released a guide on effective Context Engineering for AI Agents — a reminder that context is a critical yet limited resource. The quality of an agent often depends less on the model itself and more on how its context is structured and managed. Even a weaker LLM can perform well with the right […]

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In this tutorial, we explore the Advanced Model Context Protocol (MCP) and demonstrate how to use it to address one of the most unique challenges in modern AI systems: enabling real-time interaction between AI models and external data or tools. Traditional models operate in isolation, limited to their training data, but through MCP, we create […]

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Researchers from Stanford, EPFL, and UNC introduce Weak-for-Strong Harnessing, W4S, a new Reinforcement Learning RL framework that trains a small meta-agent to design and refine code workflows that call a stronger executor model. The meta-agent does not fine tune the strong model, it learns to orchestrate it. W4S formalizes workflow design as a multi turn […]

The post Weak-for-Strong (W4S): A Novel Reinforcement Learning Algorithm that Trains a weak Meta Agent to Design Agentic Workflows with Stronger LLMs appeared first on MarkTechPost.

Microsoft Research proposes BitNet Distillation, a pipeline that converts existing full precision LLMs into 1.58 bit BitNet students for specific tasks, while keeping accuracy close to the FP16 teacher and improving CPU efficiency. The method combines SubLN based architectural refinement, continued pre training, and dual signal distillation from logits and multi head attention relations. Reported […]

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Kong has open-sourced Volcano, a TypeScript SDK that composes multi-step agent workflows across multiple LLM providers with native Model Context Protocol (MCP) tool use. The release coincides with broader MCP capabilities in Kong AI Gateway and Konnect, positioning Volcano as the developer SDK in an MCP-governed control plane. What Volcano provides? Volcano exposes a compact, […]

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Are your LLM code benchmarks actually rejecting wrong-complexity solutions and interactive-protocol violations, or are they passing under-specified unit tests? A team of researchers from UCSD, NYU, University of Washington, Princeton University, Canyon Crest Academy, OpenAI, UC Berkeley, MIT, University of Waterloo, and Sentient Labs introduce AutoCode, a new AI framework that lets LLMs create and […]

The post AutoCode: A New AI Framework that Lets LLMs Create and Verify Competitive Programming Problems, Mirroring the Workflow of Human Problem Setters appeared first on MarkTechPost.

Reinforcement Learning RL post-training is now a major lever for reasoning-centric LLMs, but unlike pre-training, it hasn’t had predictive scaling rules. Teams pour tens of thousands of GPU-hours into runs without a principled way to estimate whether a recipe will keep improving with more compute. A new research from Meta, UT Austin, UCL, Berkeley, Harvard, […]

The post Sigmoidal Scaling Curves Make Reinforcement Learning RL Post-Training Predictable for LLMs appeared first on MarkTechPost.

In this tutorial, we build a compact, efficient framework that demonstrates how to convert tool documentation into standardized, callable interfaces, register those tools in a central system, and execute them as part of an automated pipeline. As we move through each stage, we create a simple converter, design mock bioinformatics tools, organize them into a […]

The post A Coding Implementation to Build a Unified Tool Orchestration Framework from Documentation to Automated Pipelines appeared first on MarkTechPost.

How do you convert complex, multilingual documents—dense layouts, small scripts, formulas, charts, and handwriting—into faithful structured Markdown/JSON with state-of-the-art accuracy while keeping inference latency and memory low enough for real deployments?Baidu’s PaddlePaddle group has released PaddleOCR-VL, a 0.9B-parameter vision-language model designed for end-to-end document parsing across text, tables, formulas, charts, and handwriting. The core model […]

The post Baidu’s PaddlePaddle Team Releases PaddleOCR-VL (0.9B): a NaViT-style + ERNIE-4.5-0.3B VLM Targeting End-to-End Multilingual Document Parsing appeared first on MarkTechPost.

A team of researchers from Google Research, Google DeepMind, and Yale released C2S-Scale 27B, a 27B parameter foundation model for single-cell analysis built on Gemma-2. The model formalizes single-cell RNA-seq (scRNA-seq) profiles as ‘cell sentences‘, ordered lists of gene symbol, so that a language model can natively parse and reason over cellular states. Beyond benchmarking […]

The post Google AI Releases C2S-Scale 27B Model that Translate Complex Single-Cell Gene Expression Data into ‘cell sentences’ that LLMs can Understand appeared first on MarkTechPost.

Agentic systems are stochastic, context-dependent, and policy-bounded. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities and provides weak audit trails. Developer teams need protocol-accurate conversations, explicit policy checks, and machine-readable evidence that can gate releases with confidence. Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI agents over […]

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In this tutorial, we build an AI-powered cryptographic agent system that combines the strength of classical encryption with adaptive intelligence. We design agents capable of performing hybrid encryption with RSA and AES, generating digital signatures, detecting anomalies in message patterns, and intelligently recommending key rotations. As we progress, we witness these autonomous agents securely establish […]

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What would you build if you could run Reinforcement Learning (RL) post-training on a 32B LLM in 4-bit NVFP4—on a single H100—with BF16-level accuracy and 1.2–1.5× step speedups? NVIDIA researchers (with collaborators from MIT, HKU, and Tsinghua) have open-sourced QeRL (Quantization-enhanced Reinforcement Learning), a training framework that pushes Reinforcement Learning (RL) post-training into 4-bit FP4 […]

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