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 ...
XDA Developers on MSN

After self-hosting LLMs for a year, I realized that models are not the real bottleneck

I stopped upgrading models and fixed my prompting instead.
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 ...
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 ...
IEEE

Exploration of Gearbox Multi-Step Remaining Useful Life Prediction based on LLM and PEFT

Abstract: Gearboxes are essential components in mechanical systems, and accurately predicting their remaining useful life (RUL) is vital for implementing effective predictive maintenance strategies.