**

Zhang Xu: Innovation and Expressiveness in Tang Dynasty Calligraphy

1. Abstract

Zhang Xu (張旭, fl. 8th century), courtesy name Bogao (伯高), stands as a seminal figure in the history of Chinese calligraphy, particularly celebrated for his revolutionary and highly expressive “wild cursive” (狂草) style. This report provides a comprehensive examination of Zhang Xu’s life and artistic contributions. It encompasses a detailed biography, an in-depth analysis of the unique characteristics of his calligraphic style, a review of existing academic literature concerning his work, an identification of gaps in current research, a formulation of research objectives and a problem statement, and proposals for future studies that could further illuminate his significance. The enduring impact of Zhang Xu’s innovative approach to calligraphy and his lasting influence on subsequent generations of calligraphers are also briefly considered.

**

Market Opportunities for AI Agents and Multi-AI Agent Systems in Vietnam

1. Executive Summary

Vietnam’s digital landscape is undergoing a rapid transformation, presenting significant opportunities for the adoption of advanced automation technologies such as AI Agents and Multi-AI Agent systems. This report provides a comprehensive analysis of the Vietnamese market, highlighting the immediate needs across key sectors including travel tourism, real estate, customer service, logistics, and manufacturing. The analysis reveals a strong government commitment to digital transformation and AI development, coupled with a high rate of technology adoption among businesses. While the market for AI Agents and Multi-AI Agent systems is still in its early stages, specific areas like customer service automation, personalized experiences in travel tourism, and efficiency improvements in real estate show immediate promise. This report recommends a phased approach to market entry, initially focusing on these high-potential areas with tailored strategies for marketing, pricing, and customer acquisition, ultimately positioning service providers to capitalize on the transformative power of AI in Vietnam.

LLM model introduction

https://allenai.org/blog/olmotrace

**

Continual Learning: A Review of Variational Dropout, Mixture of Experts with Prompting, and Backdoor Attacks

1. Introduction

The field of machine learning has witnessed significant advancements in recent years, enabling models to achieve remarkable performance on a wide array of tasks. However, a fundamental challenge arises when these models are deployed in dynamic environments where new data or tasks are encountered sequentially. This paradigm, known as continual learning, necessitates the ability of a model to learn from a continuous stream of information without forgetting previously acquired knowledge.1 A major impediment to achieving this goal is catastrophic forgetting, a phenomenon where the learning of new information leads to a drastic decline in performance on previously learned tasks.4 Overcoming this challenge requires specialized techniques that can maintain a delicate balance between the model’s capacity to learn new tasks (plasticity) and its ability to retain old knowledge (stability).4

Research Proposal: MoE-JEPA World Models for Efficient Reinforcement Learning and Planning

Abstract

Current AI research emphasizes the development of sophisticated world models capable of understanding complex dynamics, particularly from video data, often leveraging self-supervised learning (SSL) for representation extraction. Predictive models in abstract spaces (like JEPA) are gaining prominence over generative ones. Simultaneously, Mixture of Experts (MoE) offers a way to scale neural network capacity efficiently. This proposal outlines a research approach combining these trends: developing an Action-Conditioned Mixture-of-Experts Joint-Embedding Predictive Architecture (MoE-JEPA) world model. This model will be pre-trained using self-supervision on large video datasets to learn robust visual representations and environment dynamics. The MoE structure will allow the model to efficiently capture diverse or multi-modal dynamics within an environment by routing inputs to specialized expert sub-networks. This sophisticated world model will then be integrated into a model-based Reinforcement Learning (RL) framework to enable efficient planning and decision-making for agents (e.g., robots) interacting with complex environments. We hypothesize that this approach will lead to more accurate world models, improved sample efficiency in RL, and better generalization across tasks compared to monolithic world models.

AI benchmarks assess the performance and capabilities of models in standardized tasks.

AI benchmarks assess the performance and capabilities of models in standardized tasks.

Test your prompts, agents, and RAGs. Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.

AI benchmarks assess the performance and capabilities of models in standardized tasks.