Should I set up a personal AI agent to help with my daily tasks? As a general rule, I think relying on any kind of automation in your daily life is dangerous when taken to the extreme and potentially alienating even when used in moderation, especially with regards to personal interactions. An AI agent that organizes my task list and gathers online links for further reading? The strongest argument for not involving more generative AI tools into your daily routine, however, remains the environmental impact these models continue to have during training and output generation. With all of that in mind, I dug through WIRED's archive, published during the glorious dawn of this mess we call the internet, to find more historical context for your question.

Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that layer together to drive rich social behavioral dynamics. Here we introduce a method for accurately modeling multi-agent systems. We present Interaction Modeling with Multiplex Attention (IMMA), a forward prediction model that uses a multiplex latent graph to represent multiple independent types of interactions and attention to account for relations of different strengths. We also introduce Progressive Layer Training, a training strategy for this architecture.

Vertical AI Agents are revolutionizing industries by delivering domain-specific intelligence and tailored solutions. However, many sectors, such as manufacturing, healthcare, and logistics, demand AI systems capable of extending their intelligence into the physical world, interacting directly with objects, environments, and dynamic conditions. This need has led to the emergence of Physical AI Agents--systems that integrate cognitive reasoning, powered by specialized LLMs, with precise physical actions to perform real-world tasks. This work introduces Physical AI Agents as an evolution of shared principles with Vertical AI Agents, tailored for physical interaction. We propose a modular architecture with three core blocks--perception, cognition, and actuation--offering a scalable framework for diverse industries. Additionally, we present the Physical Retrieval Augmented Generation (Ph-RAG) design pattern, which connects physical intelligence to industry-specific LLMs for real-time decision-making and reporting informed by physical context. Through case studies, we demonstrate how Physical AI Agents and the Ph-RAG framework are transforming industries like autonomous vehicles, warehouse robotics, healthcare, and manufacturing, offering businesses a pathway to integrate embodied AI for operational efficiency and innovation.

Fully decentralized learning algorithms are still in an early stage of development. Creating modular Gossip Learning strategies is not trivial due to convergence challenges and Byzantine faults intrinsic in systems of decentralized nature. Our contribution provides a novel means to simulate custom Gossip Learning systems by leveraging the state-of-the-art Flower Framework. Specifically, we introduce GLow, which will allow researchers to train and assess scalability and convergence of devices, across custom network topologies, before making a physical deployment. The Flower Framework is selected for being a simulation featured library with a very active community on Federated Learning research. However, Flower exclusively includes vanilla Federated Learning strategies and, thus, is not originally designed to perform simulations without a centralized authority. GLow is presented to fill this gap and make simulation of Gossip Learning systems possible. Results achieved by GLow in the MNIST and CIFAR10 datasets, show accuracies over 0.98 and 0.75 respectively. More importantly, GLow performs similarly in terms of accuracy and convergence to its analogous Centralized and Federated approaches in all designed experiments.

This study investigates how the Big Five personality traits influence decision-making processes in AI agents within public spaces. Using AgentVerse framework and GPT-3.5-turbo, we simulated interactions among 10 AI agents, each embodying different dimensions of the Big Five personality traits, in a classroom environment responding to misinformation. The experiment assessed both public expressions ([Speak]) and private thoughts ([Think]) of agents, revealing significant correlations between personality traits and decision-making patterns. Results demonstrate that Openness to Experience had the strongest impact on information acceptance, with curious agents showing high acceptance rates and cautious agents displaying strong skepticism. Extraversion and Conscientiousness also showed notable influence on decision-making, while Neuroticism and Agreeableness exhibited more balanced responses. Additionally, we observed significant discrepancies between public expressions and private thoughts, particularly in agents with friendly and extroverted personalities, suggesting that social context influences decision-making behavior. Our findings contribute to understanding how personality traits shape AI agent behavior in social settings and have implications for developing more nuanced and context-aware AI systems.

Generative artificial intelligence (GenAI) holds great promise as a tool to support personalized learning. Teachers need tools to efficiently and effectively enhance content readability of educational texts so that they are matched to individual students reading levels, while retaining key details. Large Language Models (LLMs) show potential to fill this need, but previous research notes multiple shortcomings in current approaches. In this study, we introduced a generalized approach and metrics for the systematic evaluation of the accuracy and consistency in which LLMs, prompting techniques, and a novel multi-agent architecture to simplify sixty informational reading passages, reducing each from the twelfth grade level down to the eighth, sixth, and fourth grade levels. We calculated the degree to which each LLM and prompting technique accurately achieved the targeted grade level for each passage, percentage change in word count, and consistency in maintaining keywords and key phrases (semantic similarity). One-sample t-tests and multiple regression models revealed significant differences in the best performing LLM and prompt technique for each of the four metrics. Both LLMs and prompting techniques demonstrated variable utility in grade level accuracy and consistency of keywords and key phrases when attempting to level content down to the fourth grade reading level. These results demonstrate the promise of the application of LLMs for efficient and precise automated text simplification, the shortcomings of current models and prompting methods in attaining an ideal balance across various evaluation criteria, and a generalizable method to evaluate future systems.

We propose a novel cooperative multi-agent reinforcement learning (MARL) approach for networked agents. In contrast to previous methods that rely on complete state information or joint observations, our agents must learn how to reach shared objectives under partial observability. During training, they collect individual rewards and approximate a team value function through local communication, resulting in cooperative behavior. To describe our problem, we introduce the networked dynamic partially observable Markov game framework, where agents communicate over a switching topology communication network. Our distributed method, DNA-MARL, uses a consensus mechanism for local communication and gradient descent for local computation. DNA-MARL increases the range of the possible applications of networked agents, being well-suited for real world domains that impose privacy and where the messages may not reach their recipients. We evaluate DNA-MARL across benchmark MARL scenarios. Our results highlight the superior performance of DNA-MARL over previous methods.

Mobile robot fleets are currently used in different scenarios such as medical environments or logistics. The management of these systems provides different challenges that vary from the control of the movement of each robot to the allocation of tasks to be performed. Task Allocation (TA) problem is a key topic for the proper management of mobile robot fleets to ensure the minimization of energy consumption and quantity of necessary robots. Solutions on this aspect are essential to reach economic and environmental sustainability of robot fleets, mainly in industry applications such as warehouse logistics. The minimization of energy consumption introduces TA problem as an optimization issue which has been treated in recent studies. This work focuses on the analysis of current trends in solving TA of mobile robot fleets. Main TA optimization algorithms are presented, including novel methods based on Artificial Intelligence (AI). Additionally, this work showcases most important results extracted from simulations, including frameworks utilized for the development of the simulations. Finally, some conclusions are obtained from the analysis to target on gaps that must be treated in the future.

The proliferation of misinformation on social media platforms has highlighted the need to understand how individual personality traits influence susceptibility to and propagation of misinformation. This study employs an innovative agent-based modeling approach to investigate the relationship between personality traits and misinformation dynamics. Using six AI agents embodying different dimensions of the Big Five personality traits (Extraversion, Agreeableness, and Neuroticism), we simulated interactions across six diverse misinformation topics. The experiment, implemented through the AgentScope framework using the GLM-4-Flash model, generated 90 unique interactions, revealing complex patterns in how personality combinations affect persuasion and resistance to misinformation. Our findings demonstrate that analytical and critical personality traits enhance effectiveness in evidence-based discussions, while non-aggressive persuasion strategies show unexpected success in misinformation correction. Notably, agents with critical traits achieved a 59.4% success rate in HIV-related misinformation discussions, while those employing non-aggressive approaches maintained consistent persuasion rates above 40% across different personality combinations. The study also revealed a non-transitive pattern in persuasion effectiveness, challenging conventional assumptions about personality-based influence. These results provide crucial insights for developing personality-aware interventions in digital environments and suggest that effective misinformation countermeasures should prioritize emotional connection and trust-building over confrontational approaches. The findings contribute to both theoretical understanding of personality-misinformation dynamics and practical strategies for combating misinformation in social media contexts.

Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significant risks. We propose a tactical separation approach leveraging shared scheduling protocols, originally designed for Ethernet networks and operating systems, to coordinate access to these intersections. Using a decentralized Markov decision process framework, the proposed approach enables aircraft to autonomously adjust their speed and timing as they navigate these critical areas, maintaining safe separation without a central controller. We evaluate the effectiveness of this approach in simulated UAM scenarios, demonstrating its ability to reduce separation violations to zero while acknowledging trade-offs in flight times as traffic density increases. Additionally, we explore the impact of non-compliant aircraft, showing that while shared scheduling protocols can no longer guarantee safe separation, they still provide significant improvements over systems without scheduling protocols.