Denny's Announces $620 Million Take-Private Deal With TriArtisan-Led Group

Denny's has transitioned into private ownership, while the parent company of Pizza Hut is contemplating a sale of the struggling chain. This move comes as the industry faces significant changes driven by evolving consumer preferences.

The upcoming vote on Elon Musk's pay plan is crucial for Tesla's future, as it could determine his level of influence within the company. Supporters argue that the proposed trillion-dollar package is necessary to keep Musk engaged, while critics believe it grants him excessive power. This debate highlights the ongoing tension between Musk and investors, making it a pivotal moment for Tesla's governance and direction.

Recent advancements in LLM Multi-Agent Systems are making it easier to manage numerous tools and sub-agents effectively. The introduction of Tool-to-Agent Retrieval aims to enhance agent selection by providing a clearer understanding of tool functionalities, leading to better orchestration and improved performance.

Tool Zero introduces an innovative approach to training language models using pure reinforcement learning from scratch. This method aims to enhance the capabilities of language models for complex tasks, overcoming the limitations of traditional supervised fine-tuning that often struggles with unfamiliar scenarios.

This article explores the advantages of deep models over shallow ones in a framework that doesn't depend on specific network implementations. It discusses how deep models can be understood as abstract state-transition semigroups and presents a bias-variance decomposition that highlights the role of depth in determining variance.

This article presents a groundbreaking model called the Structurally Adaptive Predictive Inference Network (SAPIN), which draws inspiration from biological neural cultures. Unlike traditional neural networks that use global backpropagation, SAPIN employs active inference principles to enhance learning and adaptability, showcasing a promising direction for future computational models.

A new approach to deep reinforcement learning tackles the challenges posed by non-stationary environments. By focusing on maintaining the flexibility of the critic network and enhancing exploration strategies, this method aims to improve stability and performance in dynamic settings.

VidEmo introduces a new approach to understanding emotions in videos, leveraging advancements in video large language models. This innovative method aims to tackle the complexities of emotional analysis, addressing the dynamic nature of emotions and their dependence on various cues.