Machine Learning

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An Implementation of IWE’s Context Bridge as an AI-Powered Knowledge Graph with Agentic RAG, OpenAI Function Calling, and Graph Traversal

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In this tutorial, we implement IWE: an open-source, Rust-powered personal knowledge management system that treats markdown notes as a navigable knowledge graph. Since IWE is a CLI/LSP tool designed for local editors. We build a realistic developer knowledge base from scratch, wire up wiki-links and markdown links into a directed graph, and then walk through […]

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Meta Releases TRIBE v2: A Brain Encoding Model That Predicts fMRI Responses Across Video, Audio, and Text Stimuli

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Neuroscience has long been a field of divide and conquer. Researchers typically map specific cognitive functions to isolated brain regions—like motion to area V5 or faces to the fusiform gyrus—using models tailored to narrow experimental paradigms. While this has provided deep insights, the resulting landscape is fragmented, lacking a unified framework to explain how the

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Google Releases Gemini 3.1 Flash Live: A Real-Time Multimodal Voice Model for Low-Latency Audio, Video, and Tool Use for AI Agents

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Google has released Gemini 3.1 Flash Live in preview for developers through the Gemini Live API in Google AI Studio. This model targets low-latency, more natural, and more reliable real-time voice interactions, serving as Google’s ‘highest-quality audio and speech model to date.’ By natively processing multimodal streams, the release provides a technical foundation for building

Google Releases Gemini 3.1 Flash Live: A Real-Time Multimodal Voice Model for Low-Latency Audio, Video, and Tool Use for AI Agents Read More »

AI system learns to keep warehouse robot traffic running smoothly

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Civil and environmental engineering, Computer science and technology, Electrical engineering and computer science (EECS), IDSS, Laboratory for Information and Decision Systems (LIDS), Logistics, Machine Learning, MIT Schwarzman College of Computing, Research, Robotics, School of Engineering, Supply chains

Inside a giant autonomous warehouse, hundreds of robots dart down aisles as they collect and distribute items to fulfill a steady stream of customer orders. In this busy environment, even small traffic jams or minor collisions can snowball into massive slowdowns.To avoid such an avalanche of inefficiencies, researchers from MIT and the tech firm Symbotic

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Augmenting citizen science with computer vision for fish monitoring

Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), ai, AI (Artificial Intelligence), Animals, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, data, Ecology, Electrical engineering and computer science (EECS), Environment, Machine Learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT Sea Grant, National Science Foundation (NSF), Oceanography and ocean engineering, Research, School of Engineering, Undergraduate Research Opportunities Program (UROP), Video, Volunteering, outreach, public service, Water

Each spring, river herring populations migrate from Massachusetts coastal waters to begin their annual journey up rivers and streams to freshwater spawning habitat. River herring have faced severe population declines over the past several decades, and their migration is extensively monitored across the region, primarily through traditional visual counting and volunteer-based programs. Monitoring fish movement and understanding

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Wristband enables wearers to control a robotic hand with their own movements

ai, AI (Artificial Intelligence), Artificial Intelligence, Assistive technology, Augmented and virtual reality, Biomechanics, Computer vision, Human-computer interaction, Machine Learning, Mechanical engineering, Research, Robotics, School of Engineering, Sensors

The next time you’re scrolling your phone, take a moment to appreciate the feat: The seemingly mundane act is possible thanks to the coordination of 34 muscles, 27 joints, and over 100 tendons and ligaments in your hand. Indeed, our hands are the most nimble parts of our bodies. Mimicking their many nuanced gestures has

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NVIDIA AI Introduces PivotRL: A New AI Framework Achieving High Agentic Accuracy With 4x Fewer Rollout Turns Efficiently

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Post-training Large Language Models (LLMs) for long-horizon agentic tasks—such as software engineering, web browsing, and complex tool use—presents a persistent trade-off between computational efficiency and model generalization. While Supervised Fine-Tuning (SFT) is computationally inexpensive, it frequently suffers from out-of-domain (OOD) performance degradation and struggles to generalize beyond its training distribution. Conversely, end-to-end reinforcement learning (E2E

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Paged Attention in Large Language Models LLMs

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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 per request based on the maximum sequence length, which leads to significant unused space and limits concurrency. Paged Attention

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

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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 research team introduces TinyLoRA, a parameterization that can scale down to a single trainable parameter under extreme sharing settings. Using this method on a

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

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

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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 model produces redundant embeddings to trivially satisfy prediction objectives. Current approaches attempt to prevent this by relying on complex heuristics: they

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