It’s here. Run local AI models on your device. More in Bleeding Edge classroom on Monday. For now, the press release version: https://techcrunch.com/2025/05/31/google-quietly-released-an-app-that-lets-you-download-and-run-ai-models-locally/

Find them in the Bleeding Edge Classroom

https://youtu.be/CFo1iTd_Cc8?si=exiy3sx1oWg_FiWC

--- ### **OpenAI o3-mini: Cost-Efficient Reasoning Model** OpenAI launched **o3-mini**, its most cost-efficient reasoning model optimized for STEM tasks (science, math, coding). Key features: - **24% faster** than o1-mini (7.7s avg response vs 10.16s) with **39% fewer errors** [1][12][15] - Supports **structured outputs**, function calling, and three reasoning effort modes (low/medium/high) [22][119] - Available to **free ChatGPT users** via "Reason" mode and API ($1.10/million input tokens) [116][120] - Outperforms o1-mini in 56% of tests but trails DeepSeek-R1 in cost efficiency ($0.55/million tokens) [15][118] --- ### **Alibaba's Qwen2.5-Plus Update** Alibaba upgraded its **Qwen Chat** with: - **Qwen2.5-Plus-0125-Exp** model using advanced post-training techniques [2][24] - **10,000-character text input** and PDF/DOCX file support [2][128] - Flexible mode switching (web search, normal, etc.) in a single session [2][126] - Outperforms GPT-4o and Gemini in document analysis but lags in multilingual tasks [24][129] --- ### **OpenAI's Deep Research Agent** New **AI research agent** synthesizes web data into reports: - Powered by **o3 model**, analyzes text/images/PDFs in minutes [3][131][137] - Generates citations and summaries, available for **ChatGPT Pro** users [44][135] - 5–30 minutes per query, with lower hallucination rates than ChatGPT [137] --- ## **⚡️ Trending Signals** ### **Google DeepMind: RL > Supervised Fine-Tuning** - **SCoRe** (Self-Correction via RL) improves math/coding accuracy by 15.6% and 9.1% over supervised methods [4][48] - RL enables **self-correction traces**, reducing bias and distributional shift [53][140] ### **NVIDIA Eagle2-9B Vision-Language Model** - **92.6% accuracy on DocVQA**, surpassing GPT-4V (88.4%) [5][59] - Trained on 180+ sources with transparent data strategy [64][68] - 80% smaller dataset (4.6M samples) maintains SOTA performance [5] ### **Meta's EvalPlanner for LLM Judges**