Crypto Briefing

MIT’s MeMo framework boosts LLM performance by 26% without retraining

MIT's MeMo framework trains a compact memory model that boosts LLM performance by up to 26.73% without retraining, with major implications for crypto AI agents.

Researchers automated LLM reasoning strategy design and cut token usage by 69.5%

Researchers from Meta and Google built AutoTTS to automatically discover optimal LLM reasoning strategies, cutting token ...
InfoWorld

Taming the generative AI back end

Writing code that interacts with LLM services requires bridging two different worlds. Use these tips and techniques to bind ...
Hosted on MSN

How to build front porch steps

DIYer and woodworker April Wilkerson builds front porch steps as a functional home improvement project. 'Incredible honour': One Nation surges ahead of Labor Omega-3 supplements linked to disturbing ...
SciTech Daily

Scientists Build a Living AI Device Using Real Brain Cells

Artist’s rendering of a biocomputing device that combines biological neurons with advanced electronics into a network that can be programmed to recognize patterns. Credit: Kate Zvorykina/Ella Maru ...
IEEE

Building Energy Management Systems with LLM Agent Enhanced Natural Language Policy Explanation

Abstract: Building Energy Management System (BEMS) is important for optimizing energy usage. However, existing BEMS often lack natural language explanations, making it difficult for residents to ...
IEEE

Two-Step RAG for Metadata Filtering and Statistical LLM Evaluation

Abstract: This study addresses a limitation in Retrieval-Augmented Generation (RAG) systems: poor retrieval accuracy when vague prompts or metadata are missing. We propose the Two-Step RAG method to ...
Ars Technica

Mozilla says 271 vulnerabilities found by Mythos have “almost no false positives”

The disbelief was palpable when Mozilla’s CTO last month declared that AI-assisted vulnerability detection meant “zero-days are numbered” and “defenders finally have a chance to win, decisively.” ...
sei.cmu

The ELM Library: An LLM Evaluation Toolset

Turri, V., Schieber, N., Loughin, C., and Brooks, T., 2026: The ELM Library: An LLM Evaluation Toolset. Carnegie Mellon University, Software Engineering Institute's ...