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…
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…
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…
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. Why Volcano SDK? because 9 lines of code…
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…
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,…
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…
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…
A team of researchers from Google Research, Google DeepMind, and Yale released C2S-Scale 27B, a 27-billion-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 symbols—so that a language model can natively parse and reason over cellular states. Beyond benchmarking gains, the research…
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…
