A new paper by researchers from Google Research and UC Berkeley shows that a simple sampling-based search approach can enhance the reasoning abilities of large language models (LLMs) without needing specialized training or complex architectures.
This article explores the Model Context Protocol (MCP), an open protocol designed to standardize AI interaction with tools and data, addressing the fragmentation in AI agent ecosystems. It details current use cases, future possibilities, and challenges in adopting MCP.
This study demonstrates that neural activity in the human brain aligns linearly with the internal contextual embeddings of speech and language within large language models (LLMs) as they process everyday conversations.
ByteDance Research has released DAPO (Dynamic Sampling Policy Optimization), an open-source reinforcement learning system for LLMs, aiming to improve reasoning abilities and address reproducibility issues. DAPO includes innovations like Clip-Higher, Dynamic Sampling, Token-level Policy Gradient Loss, and Overlong Reward Shaping, achieving a score of 50 on the AIME 2024 benchmark with the Qwen2.5-32B model.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
This document details how to use function calling with Mistral AI models to connect to external tools and build more complex applications, outlining a four-step process: User query & tool specification, Model argument generation, User function execution, and Model final answer generation.
The Gemini API documentation provides comprehensive information about Google's Gemini models and their capabilities. It includes guides on generating content with Gemini models, native image generation, long context exploration, and generating structured outputs. The documentation offers examples in Python, Node.js, and REST for using the Gemini API, covering various applications like text and image generation, and integrating Gemini in Google AI Studio.
Goose is a local, extensible, open-source AI agent designed to automate complex engineering tasks. It can build projects from scratch, write and execute code, debug failures, orchestrate workflows, and interact with external APIs. Goose is flexible, supporting any LLM and seamlessly integrating with MCP-enabled APIs, making it a powerful tool for developers to accelerate innovation.
The article discusses how Visa leverages retrieval-augmented generation (RAG) and deep learning to enhance operations. It describes Visa's 'Secure ChatGPT,' which offers a multi-model interface for secure internal use, and how RAG improves policy-related data retrieval. The article also explores Visa's data infrastructure and AI's role in fraud prevention.
Alibaba's Qwen team aims to find out with its latest release, QwQ. Despite having a fraction of DeepSeek R1's claimed 671 billion parameters, Alibaba touts its comparatively compact 32-billion 'reasoning' model as outperforming R1 in select math, coding, and function-calling benchmarks.
