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- ⏰ Featured AIs: NVIDIA AI Releases OpenMath-Nemotron-32B and 14B-Kaggle....
⏰ Featured AIs: NVIDIA AI Releases OpenMath-Nemotron-32B and 14B-Kaggle....
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Dive into the hottest AI breakthroughs of the week—handpicked just for you!
Machine Learning
NVIDIA AI Releases OpenMath-Nemotron-32B and 14B-Kaggle: Advanced AI Models for Mathematical Reasoning that Secured First Place in the AIMO-2 Competition and Set New Benchmark Records
NVIDIA has introduced OpenMath-Nemotron-32B and OpenMath-Nemotron-14B-Kaggle, each meticulously engineered to excel in mathematical reasoning tasks. Building on the success of the Qwen family of transformer models, these Nemotron variants utilize large-scale fine-tuning on an extensive corpus of mathematical problems, collectively known as the OpenMathReasoning dataset. The design philosophy underlying both releases centers on maximizing accuracy across competitive benchmarks while maintaining practical considerations for inference speed and resource efficiency. By offering multiple model sizes and configurations, NVIDIA provides researchers and practitioners with a flexible toolkit for integrating advanced math capabilities into diverse applications…..
⇧ 1,249 Likes
Computer Vision
Microsoft Research Introduces MMInference to Accelerate Pre-filling for Long-Context Vision-Language Models
Researchers from the University of Surrey and Microsoft have introduced MMInference, a dynamic, sparse attention method designed to accelerate the pre-filling stage of long-context VLMs. By identifying grid-like sparsity patterns in video inputs and distinct modality boundaries, MMInference applies permutation-based strategies to optimize attention computation. It dynamically constructs sparse distributions for each input and utilizes custom GPU kernels for enhanced efficiency, all without requiring modifications to existing models. Tested on benchmarks like Video QA, Captioning, and Vision-NIAH, MMInference achieved up to 8.3× speedup at 1M tokens, outperforming previous methods while maintaining high accuracy across multiple state-of-the-art VLMs.......
⇧ 1,749 Likes
Important AI News 🔥 🔥 🔥
Agentic AI & MCP
🧵 Skywork AI Advances Multimodal Reasoning: Introducing Skywork R1V2 with Hybrid Reinforcement Learning
⇧2,500 Likes
LLM Evaluation
📢 Google DeepMind Research Introduces QuestBench: Evaluating LLMs’ Ability to Identify Missing Information in Reasoning Tasks
⇧2,350 Likes
Agentic AI
🚨 AgentA/B: A Scalable AI System Using LLM Agents that Simulate Real User Behavior to Transform Traditional A/B Testing on Live Web Platforms
⇧2,134 Likes
Language Models
💡 LLMs Can Now Simulate Massive Societies: Researchers from Fudan University Introduce SocioVerse, an LLM-Agent-Driven World Model for Social Simulation with a User Pool of 10 Million Real Individuals
⇧1,900 Likes
LLM Evaluation
🧲 This AI Paper from China Proposes a Novel Training-Free Approach DEER that Allows Large Reasoning Language Models to Achieve Dynamic Early Exit in Reasoning
⇧1,500 Likes
Agentic AI & Open Source
🧵 ByteDance Introduces QuaDMix: A Unified AI Framework for Data Quality and Diversity in LLM Pretraining
⇧1,300 Likes
Computer Vision
Meta AI Introduces Token-Shuffle: A Simple AI Approach to Reducing Image Tokens in Transformers
⇧ 4,670 Likes
Meta AI introduces Token-Shuffle, a method designed to reduce the number of image tokens processed by Transformers without altering the fundamental next-token prediction reach. The key insight underpinning Token-Shuffle is the recognition of dimensional redundancy in visual vocabularies used by multimodal large language models (MLLMs). Visual tokens, typically derived from vector quantization (VQ) models, occupy high-dimensional spaces but carry a lower intrinsic information density compared to text tokens. Token-Shuffle exploits this by merging spatially local visual tokens along the channel dimension before Transformer processing and subsequently restoring the original spatial structure after inference. This token fusion mechanism allows AR models to handle higher resolutions with significantly reduced computational cost while maintaining visual fidelity.......
⇧3,200 Likes
Code and Learn
Hands-on-Coding </>
🖥️ A Coding Implementation with Arcade: Integrating Gemini Developer API Tools into LangGraph Agents for Autonomous AI Workflows
Arcade transforms your LangGraph agents from static conversational interfaces into dynamic, action-driven assistants by providing a rich suite of ready-made tools, including web scraping and search, as well as specialized APIs for finance, maps, and more. In this tutorial, we will learn how to initialize ArcadeToolManager, fetch individual tools (such as Web.ScrapeUrl) or entire toolkits, and seamlessly integrate them into Google’s Gemini Developer API chat model via LangChain’s ChatGoogleGenerativeAI. With a few steps, we installed dependencies, securely loaded your API keys, retrieved and inspected your tools, configured the Gemini model, and spun up a ReAct-style agent complete with checkpointed memory. Throughout, Arcade’s intuitive Python interface kept your code concise and your focus squarely on crafting powerful, real-world workflows, no low-level HTTP calls or manual parsing required....….
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