May 20, 2025

MAGI-1: Autoregressive Video Generation at Scale

MAGI-1 is a world model that generates videos by autoregressively predicting sequences of video chunks, achieving high temporal consistency and scalability through innovative algorithms and infrastructure.

The Behavior of LLMs in Hiring Decisions: Systemic Biases in Candidate Selection

LLMs exhibit systemic gender bias, favoring female candidates in hiring decisions, selecting them 56.9% of the time despite identical qualifications, indicating a significant deviation from expected rational behavior.

Introducing Veo 3 and Imagen 4, and a new tool for filmmaking called Flow

New generative media models like Veo 3 and Imagen 4 enable the creation of high-quality images, videos, and music, enhancing artistic expression and creativity through advanced AI tools.

I got fooled by AI-for-science hype–here's what it taught me

AI's application in plasma physics research revealed significant limitations, as methods like PINNs often performed worse than traditional numerical techniques, challenging the narrative of AI's transformative potential in science.

Deep Learning Is Applied Topology

Deep learning leverages topology to manipulate data surfaces, allowing complex datasets to be separated through transformations akin to bending and stretching, as illustrated by neural network operations.

Gemma 3n preview: Mobile-first AI

Gemma 3n is a mobile-first AI model designed for real-time applications, enabling developers to create efficient, on-device experiences that respect user privacy and operate without internet connectivity.

llm-d, Kubernetes native distributed inference

llm-d is a Kubernetes-native framework designed for high-performance distributed LLM inference, optimizing deployment with features like KV-cache aware routing and disaggregated serving to enhance efficiency and reduce latency.

Robin: A multi-agent system for automating scientific discovery

Robin is the first multi-agent system that automates the entire scientific discovery process, integrating literature search and data analysis to generate and validate hypotheses autonomously.

AI's energy footprint

AI's energy consumption is escalating rapidly, with projections indicating that by 2028, AI could consume as much electricity annually as 22% of all US households, driven by the increasing integration of AI into daily applications and the construction of energy-intensive data centers.

Thinkless: LLM Learns When to Think

Thinkless introduces a learnable framework that enables LLMs to choose between short-form and long-form reasoning, optimizing performance based on task complexity and model capability.

[P] OpenEvolve: Open Source Implementation of DeepMind's AlphaEvolve System

OpenEvolve is an open-source framework that evolves entire codebases using LLMs, enabling the discovery and optimization of algorithms through a structured pipeline of code generation, evaluation, and selection.

NVIDIA-Powered Supercomputer to Enable Quantum Leap for Taiwan Research

The new AI supercomputer at Taiwan's National Center for High-Performance Computing, built by ASUS, will deliver over 8x more AI performance than its predecessor, Taiwania 2, significantly enhancing research capabilities in climate science, quantum computing, and large language models.

Questioning Representational Optimism in Deep Learning

The Fractured Entangled Representation Hypothesis (FER) posits that while both evolved and SGD-trained neural networks can produce similar outputs, their internal representations differ significantly, with SGD networks exhibiting a disorganized structure that may hinder generalization and creativity.

NVIDIA Grows Quantum Computing Ecosystem With Taiwan Manufacturers and Supercomputing

NVIDIA is enhancing its quantum computing ecosystem by collaborating with Taiwanese manufacturers like Compal and Quanta, integrating AI supercomputing hardware to accelerate quantum research and development.

[R] [Q] Why does RoPE need to be decoupled in DeepSeek V2/V3's MLA? I don't get why it prevents prefix key reuse

RoPE's decoupling in DeepSeek V2/V3's MLA is essential because it prevents the absorption of projection matrices, which is crucial for efficient key reuse during inference.