AI & ML Research 24 Hours

Last updated: September 5, 2026, 2:12 PM ET

AI & ML Research

A new visual guide explains why transformers require positional encoding for time-series tasks, detailing how scalar observations feed self-attention and how positional information restores sequence order. Separately, researchers propose using reduced order models to improve reinforcement learning for complex physics, enabling more efficient dynamical system transfer learning across tasks.

Systems & Infrastructure

As AI inference scales, memory and storage architectures are being rethought. The era demands systems capable of analyzing millions of data points in real time—for applications like healthcare research and intelligent assistants—pushing new designs that prioritize bandwidth and latency over traditional compute-centric models.