InfoWorld

Multi-token prediction technique triples LLM inference speed without auxiliary draft models

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.

Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
Scientific American

This mathematician proved a brilliant theorem to justify his social awkwardness

George Pólya’s random walk theorem absolved him of being a lurker and revealed how the laws of chance interact with physical ...
IEEE

A Random Pulse Position-Based Selective Noise Cancellation Modulation Method for SVPWM Driven PMSMs

Abstract: Permanent magnetsynchronous motors (PMSMs) are usually driven by the SVPWM technique, which leads to annoying sideband noise near the switching frequency. Traditional random modulation ...
IEEE

Findings of Mega: Math explanation with LLMs using the socratic method for active learning

Abstract: This article presents an intervention study on the effects of the combined methods of 1) the Socratic method, 2) chain-of-thought (CoT) reasoning, 3) simplified gamification, and 4) ...
GitHub

Phi-OTDR_dataset_and_codes

This dataset contains six types of Phi-OTDR events, including background noises, digging, knocking, shaking, watering and walking, in total of 15,419 samples. The data is divided into training set and ...