Image2Net: Datasets, Benchmark and Hybrid Framework to Convert Analog Circuit Diagrams into Netlists

A recent study utilized Large Language Model (LLM) based Multi-Agent Systems to simulate adversarial debates, revealing that workplace toxicity significantly increases conversation duration by approximately 25%. This research provides a controlled environment to quantify the inefficiencies caused by incivility in organizational settings, addressing a critical gap in understanding its impact on operational efficiency.

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. This benchmark addresses unique challenges such as extreme time-sensitivity and the need for data synthesis from specialized sources, reflecting real-world analyst workflows through a monthly set of 50 expertly designed questions.

A new dataset named Aerial-D has been introduced for generalized referring expression segmentation in aerial imagery, comprising 37,288 images and over 1.5 million referring expressions. This dataset addresses the unique challenges posed by aerial photos, such as varying spatial resolutions and high object densities, which complicate visual localization tasks in computer vision.

An innovative AI-powered Autonomous Underwater Vehicle (AUV) system has been developed to enhance sea exploration and scientific research, addressing challenges such as extreme conditions and limited visibility. The system utilizes advanced technologies including YOLOv12 Nano for real-time object detection and a Large Language Model (GPT-4o Mini) for generating structured reports on underwater findings.

A new approach to sentence simplification has been introduced, utilizing Large Language Models (LLMs) as judges to create policy-aligned training data, eliminating the need for expensive human annotations or parallel corpora. This method allows for tailored simplification systems that can adapt to various policies, enhancing readability while maintaining meaning.

A recent study has examined the representation distance bias in the Bradley-Terry (BT) loss used for reward models in large language models (LLMs). The research highlights that the gradient norm of BT-loss is influenced by both the prediction error and the representation distance between chosen and rejected responses, which can lead to misalignment in learning.

EasySpec has been introduced as a layer-parallel speculative decoding strategy aimed at enhancing the efficiency of multi-GPU utilization in large language model (LLM) inference. By breaking inter-layer data dependencies, EasySpec allows multiple layers of the draft model to run simultaneously across devices, reducing GPU idling during the drafting stage.

A recent study has unveiled significant privacy risks associated with the Key-Value (KV) cache used in Large Language Model (LLM) inference, revealing that attackers can reconstruct sensitive user inputs from this cache. The research introduces three attack vectors: Inversion Attack, Collision Attack, and Injection Attack, highlighting the practical implications of these vulnerabilities.