Rep-GLS: Report-Guided Generalized Label Smoothing for Robust Disease Detection

A new framework for recognizing Parkinsonian gait patterns has been developed, utilizing a multimodal approach that fuses RGB and Depth (RGB-D) data. This system employs dual YOLOv11-based encoders and a Multi-Scale Local-Global Extraction module to enhance gait analysis, particularly in challenging conditions such as low lighting or occlusion.

A recent study has unveiled significant privacy risks associated with the Key-Value (KV) cache used in Large Language Model (LLM) inference. The research highlights that attackers can reconstruct sensitive user inputs from the KV-cache, demonstrating vulnerabilities through various attack vectors, including direct Inversion, Collision, and semantic-based Injection Attacks. To address these issues, the study proposes KV-Cloak, a novel defense mechanism designed to enhance privacy during LLM operations.

CryptoBench has been introduced as the first expert-curated, dynamic benchmark aimed at evaluating the capabilities of Large Language Model (LLM) agents specifically in the cryptocurrency sector, addressing challenges such as time sensitivity and the need for data synthesis from specialized sources.

The introduction of the Intelligent Multi-Agent Debate (iMAD) framework aims to enhance the efficiency and accuracy of Large Language Model (LLM) inference by selectively triggering structured debates among LLM agents. This approach addresses the computational costs and potential inaccuracies associated with traditional Multi-Agent Debate systems, which can degrade performance by overturning correct answers.