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发布于 2026-03-25 / 2 阅读
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AI 每日资讯 - 2026-03-25

发布日期：2026-03-25

收录条目：11

1. Paged Attention in Large Language Models LLMs

摘要：When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data. In traditional setups, a large fixed memory block is reserved

2. A Coding Implementation to Design Self-Evolving Skill Engine with OpenSpace for Skill Learning, Token Efficiency, and Collective Intelligence

摘要：In this tutorial, we explore OpenSpace, a self-evolving skill engine developed by HKUDS that makes AI agents smarter, more cost-efficient, and capable of learning from every task they perform. We walk through the complet

3. This AI Paper Introduces TinyLoRA, A 13-Parameter Fine-Tuning Method That Reaches 91.8 Percent GSM8K on Qwen2.5-7B

摘要：Researchers from FAIR at Meta, Cornell University, and Carnegie Mellon University have demonstrated that large language models (LLMs) can learn to reason using a remarkably small number of trained parameters. The researc

4. Helping developers build safer AI experiences for teens

摘要：OpenAI releases prompt-based teen safety policies for developers using gpt-oss-safeguard, helping moderate age-specific risks in AI systems.

5. Update on the OpenAI Foundation

摘要：The OpenAI Foundation announces plans to invest at least $1 billion in curing diseases, economic opportunity, AI resilience, and community programs.

6. Powering product discovery in ChatGPT

摘要：ChatGPT introduces richer, visually immersive shopping powered by the Agentic Commerce Protocol, enabling product discovery, side-by-side comparisons, and merchant integration.

7. Yann LeCun’s New LeWorldModel (LeWM) Research Targets JEPA Collapse in Pixel-Based Predictive World Modeling

摘要：World Models (WMs) are a central framework for developing agents that reason and plan in a compact latent space. However, training these models directly from pixel data often leads to ‘representation collapse,’ where the

8. Meta AI’s New Hyperagents Don’t Just Solve Tasks—They Rewrite the Rules of How They Learn

摘要：The dream of recursive self-improvement in AI—where a system doesn’t just get better at a task, but gets better at learning—has long been the ‘holy grail’ of the field. While theoretical models like the Gödel Machine hav

9. Luma Labs Launches Uni-1: The Autoregressive Transformer Model that Reasons through Intentions Before Generating Images

摘要：In the field of generative AI media, the industry is transitioning from purely probabilistic pixel synthesis toward models capable of structural reasoning. Luma Labs has just released Uni-1, a foundational image model de

10. How to Design a Production-Ready AI Agent That Automates Google Colab Workflows Using Colab-MCP, MCP Tools, FastMCP, and Kernel Execution

摘要：In this tutorial, we build an advanced, hands-on tutorial around Google’s newly released colab-mcp, an open-source MCP (Model Context Protocol) server that lets any AI agent programmatically control Google Colab notebook

11. How BM25 and RAG Retrieve Information Differently?

摘要：When you type a query into a search engine, something has to decide which documents are actually relevant — and how to rank them. BM25 (Best Matching 25), the algorithm powering search engines like Elasticsearch and Luce

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