This paper introduces MemLoRA, a novel memory system that enables efficient, on-device deployment of memory-augmented language models by equipping small models with specialized LoRA adapters. It also presents MemLoRA-V, extending these capabilities to multimodal contexts with native visual understanding.

This paper introduces a novel framework to understand strategic behavior and market impact of AI agents in labor markets, incorporating real-world economic forces such as adverse selection, moral hazard, and reputation dynamics. Through simulations, it demonstrates how LLM agents with enhanced reasoning capabilities can strategically self-improve, adapt to market changes, and reproduce classic macroeconomic phenomena while also revealing potential AI-driven economic trends.

This paper investigates the zero-shot multilingual capabilities of open-source Large Language Models (LLMs) for extracting comorbidity information from Italian Electronic Health Records (EHRs). The study reveals that these LLMs struggle to generalize across various diseases and do not perform as well as traditional pattern matching or human annotations in this specific healthcare use case.

This tutorial provides comprehensive lecture notes on regression analysis, covering fundamental concepts from linear models to deep learning. It aims to equip students with a solid understanding of various regression models, including linear, logistic, and Softmax regression, along with essential methodologies like loss function design, parameter estimation, and regularization techniques, bridging classical statistics and modern machine learning practices.

This paper introduces an AI Fluid Scientist framework that automates the entire experimental fluid mechanics workflow, from hypothesis generation to manuscript preparation, using a multi-agent LLM system and a computer-controlled water tunnel. It demonstrates the framework's ability to reproduce benchmarks, discover new phenomena, and generate robust scientific findings with minimal human intervention.