This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Research into society's informal rules of conduct, or norms, has recently experienced a
surge, extending across multiple academic disciplines. Despite this growth, the theoretical …

Multi-agent collaboration with Large Language Models (LLMs) demonstrates proficiency in
basic tasks, yet its efficiency in more complex scenarios remains unexplored. In gaming …

Multi-agent reinforcement learning (MARL) has recently gained popularity and achieved
much success in different kind of games such as zero-sum, cooperative or general-sum …

Centralized Training with Decentralized Execution (CTDE) has been proven to be an
effective paradigm in cooperative multi-agent reinforcement learning (MARL). One of the …

Multi-agent reinforcement learning (MARL) methods, while effective in zero-sum or positive-
sum games, often yield suboptimal outcomes in general-sum games where cooperation is …

Learning collaborative behaviors is essential for multi-agent systems. Traditionally, multi-
agent reinforcement learning solves this implicitly through a joint reward and centralized …

Patients with inflammatory lung diseases are often additionally exposed to polycyclic
aromatic hydrocarbons like B [a] P and B [a] P-induced alterations in gene expression in …

Multi-Agent Policy Gradient (MAPG) has made significant progress in recent years.
However, centralized critics in state-of-the-art MAPG methods still face the centralized …

Social-ecological systems, in which agents interact with each other and their environment
are important both for sustainability applications and for under-standing how human …