⏰ Featured AI: Meet Google's AI Co-Scientist & IBM Releases Granite 3.2 8B and 2B Models....

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Dive into the hottest AI breakthroughs of the week—handpicked just for you!

Super Important AI News 🔥 🔥 🔥

⭐ Meet AI Co-Scientist: A Multi-Agent System Powered by Gemini 2.0 for Accelerating Scientific Discovery

📢 Microsoft AI Released LongRoPE2: A Near-Lossless Method to Extend Large Language Model Context Windows to 128K Tokens While Retaining Over 97% Short-Context Accuracy

🚨 Researchers from UCLA, UC Merced and Adobe propose METAL: A Multi-Agent Framework that Divides the Task of Chart Generation into the Iterative Collaboration among Specialized Agents

💡💡 IBM AI Releases Granite 3.2 8B Instruct and Granite 3.2 2B Instruct Models: Offering Experimental Chain-of-Thought Reasoning Capabilities

🧲 🧲 Google AI Introduces PlanGEN: A Multi-Agent AI Framework Designed to Enhance Planning and Reasoning in LLMs through Constraint-Guided Iterative Verification and Adaptive Algorithm Selection

Featured AI Update 🛡️🛡️🛡️

🔥 Meet AI Co-Scientist: A Multi-Agent System Powered by Gemini 2.0 for Accelerating Scientific Discovery

Researchers from Google Cloud AI Research, Google Research, Google DeepMind, Houston Methodist, Sequome, Fleming Initiative and Imperial College London, and Stanford University School of Medicine have proposed an AI co-scientist, a multi-agent system built on Gemini 2.0 designed to accelerate scientific discovery. It aims to uncover new knowledge and generate novel research hypotheses aligned with scientist-provided objectives. Using a “generate, debate, and evolve” approach, the AI co-scientist uses test-time compute scaling to improve hypothesis generation. Moreover, it focuses on three biomedical domains: drug repurposing, novel target discovery, and explanation of bacterial evolution mechanisms. Automated evaluations show that increased test-time computation consistently improves hypothesis quality.…..

Other AI News 🎖️🎖️🎖️

🚨 This AI Paper Introduces Agentic Reward Modeling (ARM) and REWARDAGENT: A Hybrid AI Approach Combining Human Preferences and Verifiable Correctness for Reliable LLM Training

🧿 This AI Paper Introduces UniTok: A Unified Visual Tokenizer for Enhancing Multimodal Generation and Understanding

🧩 A-MEM: A Novel Agentic Memory System for LLM Agents that Enables Dynamic Memory Structuring without Relying on Static, Predetermined Memory Operations

📢 Stanford Researchers Uncover Prompt Caching Risks in AI APIs: Revealing Security Flaws and Data Vulnerabilities

🚨 Marktechpost is inviting AI Companies/Startups/Groups to partner for its upcoming AI Magazines on ‘Open Source AI in Production’ and ‘Agentic AI’. _{(Editor’s Message)}