A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization

US-China collaboration in technology research has declined to its lowest level in 20 years, according to a study by an Australian think tank, indicating a significant shift in the global tech landscape. This decoupling is seen as a potential threat to innovation crucial for both national security and economic growth.

RealPage has agreed to a settlement with the U.S. Department of Justice (DOJ) regarding allegations that its software facilitated rent collusion among landlords, enabling them to charge above-market rates. This settlement is part of a federal antitrust case that has raised concerns about the company's impact on rental pricing across the United States.

Amazon has launched an enterprise preview of its satellite internet service, Amazon Leo, while committing $50 billion to enhance AI and high-performance computing (HPC) infrastructure for U.S. federal customers starting in 2026.

Cornell Tech has secured a $7 million investment from NASA and Schmidt Sciences aimed at modernizing arXiv, a preprint repository for scientific papers. This funding will facilitate the migration of arXiv to cloud infrastructure, upgrade its outdated codebase, and develop new tools to enhance the discovery of relevant preprints for researchers.

The United States has launched the Genesis Mission, an initiative aimed at transforming the nation's research infrastructure and securing technological leadership in artificial intelligence (AI). This mission, established through an executive order signed by President Donald Trump, focuses on utilizing federal scientific datasets to enhance AI innovation and development.

A novel approach named PocketLLM has been introduced to address the challenges of compressing large language models (LLMs) for efficient storage and transmission on edge devices. This method utilizes meta-networks to project LLM weights into discrete latent vectors, achieving significant compression ratios, such as a 10x reduction for Llama 2-7B, while maintaining accuracy.

A recent position paper emphasizes the need for literary scholars to engage with research on large language model (LLM) interpretability, suggesting that the red team could serve as a platform for this ideological struggle. The paper argues that current interpretability standards are insufficient for evaluating LLMs.

A new framework named Reading Comprehension Exercise Generation (RCEG) has been proposed to leverage large language models (LLMs) for automatically generating personalized English reading comprehension exercises. This framework utilizes fine-tuned LLMs to create content candidates, which are then evaluated by a discriminator to select the highest quality output, significantly enhancing the educational content generation process.