IEEE Rolls Out Large Language Models Virtual Training Course

Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.While the general public uses AI…

Source: IEEE Spectrum

Automatically aggregated summary — full article and all rights belong to the original publisher.

The Download: AI bottleneck debates, and BCI trials take off

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. A startup claims it broke through a bottleneck that’s holding back LLMs AI startup Subquadratic came out of stealth last month…

Source: MIT Technology Review

Automatically aggregated summary — full article and all rights belong to the original publisher.

A startup claims it broke through a bottleneck that’s holding back LLMs

Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced.…

Source: MIT Technology Review

Automatically aggregated summary — full article and all rights belong to the original publisher.

Brain-computer interface trials are taking off

This week, I covered the story of Casey Harrell—a man with ALS who is “the first power user” of a brain implant, according to the researchers who worked with him. Harrell is paralyzed and unable to speak coherently without the device. He has now spent…

Source: MIT Technology Review

Automatically aggregated summary — full article and all rights belong to the original publisher.

Learning What to Remember: Observability-Safe Memory Retention via Constrained Optimization for Long-Horizon Language Agents

arXiv:2606.10616v4 Announce Type: replace Abstract: Long-horizon language agents accumulate observations, reasoning traces, and retrieved facts exceeding context windows, making memory retention a fundamental resource-allocation problem. Existing systems treat retention as local and do not model long-term consequences under observability constraints. To fill this gap, we…

Source: cs.AI updates on arXiv.org

Automatically aggregated summary — full article and all rights belong to the original publisher.

Repurposing a Speech Classifier for Guided Diffusion-Based Speech Generation

arXiv:2606.20457v1 Announce Type: cross Abstract: Classifier guidance is a way to control diffusion generation by using a noise-conditioned classifier to steer the sampling process toward a target class. One drawback of classifier guidance is that it requires two separately trained models: a classifier and a…

Source: cs.AI updates on arXiv.org

Automatically aggregated summary — full article and all rights belong to the original publisher.

SIGMA: Search-Augmented On-Demand Knowledge Integration for Agentic Mathematical Reasoning

arXiv:2510.27568v2 Announce Type: replace Abstract: Solving mathematical reasoning problems requires not only accurate access to relevant knowledge but also careful, multi-step thinking. However, current retrieval-augmented models often rely on a single perspective, follow inflexible search strategies, and struggle to effectively combine information from multiple sources.…

Source: cs.AI updates on arXiv.org

Automatically aggregated summary — full article and all rights belong to the original publisher.