The landscape of multimodal large language models (MLLMs) has shifted from experimental ‘wrappers’—where separate vision or audio encoders are stitched […]
Category: Machine Learning
Meet A-Evolve: The PyTorch Moment For Agentic AI Systems Replacing Manual Tuning With Automated State Mutation And Self-Correction
A team of researchers associated with Amazon has released A-Evolve, a universal infrastructure designed to automate the development of autonomous […]
NVIDIA AI Unveils ProRL Agent: A Decoupled Rollout-as-a-Service Infrastructure for Reinforcement Learning of Multi-Turn LLM Agents at Scale
NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting […]
An Implementation of IWE’s Context Bridge as an AI-Powered Knowledge Graph with Agentic RAG, OpenAI Function Calling, and Graph Traversal
In this tutorial, we implement IWE: an open-source, Rust-powered personal knowledge management system that treats markdown notes as a navigable […]
Meta Releases TRIBE v2: A Brain Encoding Model That Predicts fMRI Responses Across Video, Audio, and Text Stimuli
Neuroscience has long been a field of divide and conquer. Researchers typically map specific cognitive functions to isolated brain regions—like […]
Google Releases Gemini 3.1 Flash Live: A Real-Time Multimodal Voice Model for Low-Latency Audio, Video, and Tool Use for AI Agents
Google has released Gemini 3.1 Flash Live in preview for developers through the Gemini Live API in Google AI Studio. […]
NVIDIA AI Introduces PivotRL: A New AI Framework Achieving High Agentic Accuracy With 4x Fewer Rollout Turns Efficiently
Post-training Large Language Models (LLMs) for long-horizon agentic tasks—such as software engineering, web browsing, and complex tool use—presents a persistent […]
Paged Attention in Large Language Models LLMs
When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a […]
This AI Paper Introduces TinyLoRA, A 13-Parameter Fine-Tuning Method That Reaches 91.8 Percent GSM8K on Qwen2.5-7B
Researchers from FAIR at Meta, Cornell University, and Carnegie Mellon University have demonstrated that large language models (LLMs) can learn […]
Yann LeCun’s New LeWorldModel (LeWM) Research Targets JEPA Collapse in Pixel-Based Predictive World Modeling
World Models (WMs) are a central framework for developing agents that reason and plan in a compact latent space. However, […]