Artificial intelligence (AI) systems are evolving beyond passive tools into autonomous agents capable of reasoning, adapting, and acting with minimal human intervention. Despite their growing presence, a structured framework is lacking to classify and compare these systems . This paper develops a typology of agentic AI systems, introducing eight dimensions that define their cognitive and environmental agency in an ordinal structure. Using a multi - phase methodological approach, we construct and refine this typology, which is then evaluated through a human - AI hybrid approach and further distilled into constructed types. The framework enables researchers and practitioners to analyze varying levels of agency in AI systems. By offering a structured perspective on the progression o f AI capabilities, the typology provides a foundation for assessing current systems and anticipating future developments in agentic AI.

Modern news is often comprehensive, weaving together information from diverse domains, including technology, finance, and agriculture. This very comprehensiveness creates a challenge for interpretation, as audiences typically possess specialized knowledge related to their expertise, age, or standpoint. Consequently, a reader might fully understand the financial implications of a story but fail to grasp or even actively misunderstand its legal or technological dimensions, resulting in critical comprehension gaps. In this work, we investigate how to identify these comprehension gaps and provide solutions to improve audiences' understanding of news content, particularly in the aspects of articles outside their primary domains of knowledge. We propose MADES, an agent-based framework designed to simulate societal communication. The framework utilizes diverse agents, each configured to represent a specific occupation or age group. Each agent is equipped with a memory system. These agents are then simulated to discuss the news. This process enables us to monitor and analyze their behavior and cognitive processes. Our findings indicate that the framework can identify confusions and misunderstandings within news content through its iterative discussion process. Based on these accurate identifications, the framework then designs supplementary material. We validated these outcomes using both statistical analysis and human evaluation, and the results show that agents exhibit significantly improved news understanding after receiving this supplementary material.

Learning physical interaction skills--such as dancing, handshaking, or sparring--remains a fundamental challenge for agents operating in human environments, particularly when the agent's morphology differs significantly from that of the demonstrator. Existing approaches often rely on handcrafted objectives or morphological similarity, limiting their capacity for generalization. Here, we introduce a framework that enables agents with diverse embodiments to learn whole-body interaction behaviors directly from human demonstrations. The framework extracts a compact, transferable representation of interaction dynamics--called the Embedded Interaction Graph (EIG)--which captures key spatiotemporal relationships between the interacting agents. This graph is then used as an imitation objective to train control policies in physics-based simulations, allowing the agent to generate motions that are both semantically meaningful and physically feasible. We demonstrate BuddyImitation on multiple agents, such as humans, quadrupedal robots with manipulators, or mobile manipulators and various interaction scenarios, including sparring, handshaking, rock-paper-scissors, or dancing. Our results demonstrate a promising path toward coordinated behaviors across morphologically distinct characters via cross-embodiment interaction learning. Mastering these behaviors is essential for robots, particularly non-humanoid ones, to function seamlessly in human environments. Robots may conduct daily activities such as object handover or participate in social rituals. Such interactions are critical not only for effective collaboration but also for fostering trust, acceptance, and intuitive communication between humans and machines. Beyond robotics, the ability to synthesize realistic physical interactions is also central to computer graphics and animation, where the goal is to produce lifelike and engaging character behaviors in films, games, and virtual worlds. Mastering physical interaction skills presents significant challenges due to the complex and dynamic nature of inter-agent coordination.

Constraint-based optimization is a cornerstone of robotics, enabling the design of controllers that reliably encode task and safety requirements such as collision avoidance or formation adherence. However, handcrafted constraints can fail in multi-agent settings that demand complex coordination. We introduce ReCoDe--Reinforcement-based Constraint Design--a decentralized, hybrid framework that merges the reliability of optimization-based controllers with the adaptability of multi-agent reinforcement learning. Rather than discarding expert controllers, ReCoDe improves them by learning additional, dynamic constraints that capture subtler behaviors, for example, by constraining agent movements to prevent congestion in cluttered scenarios. Through local communication, agents collectively constrain their allowed actions to coordinate more effectively under changing conditions. In this work, we focus on applications of ReCoDe to multi-agent navigation tasks requiring intricate, context-based movements and consensus, where we show that it outperforms purely handcrafted controllers, other hybrid approaches, and standard MARL baselines. We give empirical (real robot) and theoretical evidence that retaining a user-defined controller, even when it is imperfect, is more efficient than learning from scratch, especially because ReCoDe can dynamically change the degree to which it relies on this controller.

The rapid rise of Large Language Models (LLMs)-based intelligent agents underscores the need for robust, scalable evaluation frameworks. Existing methods rely on static benchmarks and labor-intensive data collection, limiting practical assessment. We introduce MCPEval, an open-source Model Context Protocol (MCP)-based framework that automates end-to-end task generation and deep evaluation of LLM agents across diverse domains. MCPEval standardizes metrics, seamlessly integrates with native agent tools, and eliminates manual effort in building evaluation pipelines. Empirical results across five real-world domains show its effectiveness in revealing nuanced, domain-specific performance. We publicly release MCPEval https://github.com/SalesforceAIResearch/MCPEval to promote reproducible and standardized LLM agent evaluation.

The concept of the Web of Agents (WoA), which transforms the static, document-centric Web into an environment of autonomous agents acting on users' behalf, has attracted growing interest as large language models (LLMs) become more capable. However, research in this area is still fragmented across different communities. Contemporary surveys catalog the latest LLM-powered frameworks, while the rich histories of Multi-Agent Systems (MAS) and the Semantic Web are often treated as separate, legacy domains. This fragmentation obscures the intellectual lineage of modern systems and hinders a holistic understanding of the field's trajectory. We present the first comprehensive evolutionary overview of the WoA. We show that modern protocols like A2A and the MCP, are direct evolutionary responses to the well-documented limitations of earlier standards like FIPA standards and OWL-based semantic agents. To systematize this analysis, we introduce a four-axis taxonomy (semantic foundation, communication paradigm, locus of intelligence, discovery mechanism). This framework provides a unified analytical lens for comparing agent architectures across all generations, revealing a clear line of descent where others have seen a disconnect. Our analysis identifies a paradigm shift in the 'locus of intelligence': from being encoded in external data (Semantic Web) or the platform (MAS) to being embedded within the agent's core model (LLM). This shift is foundational to modern Agentic AI, enabling the scalable and adaptive systems the WoA has long envisioned. We conclude that while new protocols are essential, they are insufficient for building a robust, open, trustworthy ecosystem. Finally, we argue that the next research frontier lies in solving persistent socio-technical challenges, and we map out a new agenda focused on decentralized identity, economic models, security, and governance for the emerging WoA.

We initiate the study of a repeated principal-agent problem over a finite horizon $T$, where a principal sequentially interacts with $K\geq 2$ types of agents arriving in an adversarial order. At each round, the principal strategically chooses one of the $N$ arms to incentivize for an arriving agent of unknown type. The agent then chooses an arm based on its own utility and the provided incentive, and the principal receives a corresponding reward. The objective is to minimize regret against the best incentive in hindsight. Without prior knowledge of agent behavior, we show that the problem becomes intractable, leading to linear regret. We analyze two key settings where sublinear regret is achievable. In the first setting, the principal knows the arm each agent type would select greedily for any given incentive. Under this setting, we propose an algorithm that achieves a regret bound of $O(\min\{\sqrt{KT\log N},K\sqrt{T}\})$ and provide a matching lower bound up to a $\log K$ factor. In the second setting, an agent's response varies smoothly with the incentive and is governed by a Lipschitz constant $L\geq 1$. Under this setting, we show that there is an algorithm with a regret bound of $\tilde{O}((LN)^{1/3}T^{2/3})$ and establish a matching lower bound up to logarithmic factors. Finally, we extend our algorithmic results for both settings by allowing the principal to incentivize multiple arms simultaneously in each round.

LA-RCS (LLM-agent-based robot control system) is a sophisticated robot control system designed to autonomously plan, work, and analyze the external environment based on user requirements by utilizing LLM-Agent. Utilizing a dual-agent framework, LA-RCS generates plans based on user requests, observes the external environment, executes the plans, and modifies the plans as needed to adapt to changes in the external conditions. Additionally, LA-RCS interprets natural language commands by the user and converts them into commands compatible with the robot interface so that the robot can execute tasks and meet user requests properly. During his process, the system autonomously evaluates observation results, provides feedback on the tasks, and executes commands based on real-time environmental monitoring, significantly reducing the need for user intervention in fulfilling requests. We categorized the scenarios that LA-RCS needs to perform into four distinct types and conducted a quantitative assessment of its performance in each scenario. The results showed an average success rate of 90 percent, demonstrating the system capability to fulfill user requests satisfactorily. For more extensive results, readers can visit our project page: https://la-rcs.github.io

Motivated by game-theoretic models of crowd motion dynamics, this paper analyzes a broad class of distributed games with jump diffusions within the recently developed $α$-potential game framework. We demonstrate that analyzing the $α$-Nash equilibria reduces to solving a finite-dimensional control problem. Beyond the viscosity and verification characterizations for the general games, we explicitly and in detail examine how spatial population distributions and interaction rules influence the structure of $α$-Nash equilibria in these distributed settings, and in particular for crowd motion games. Our theoretical results are supported by numerical implementations using policy gradient-based algorithms, further demonstrating the computational advantages of the $α$-potential game framework in computing Nash equilibria for general dynamic games.

As agentic platforms scale, agents are evolving beyond static roles and fixed toolchains, creating a growing need for flexible, decentralized coordination. Today's structured communication protocols (e.g., direct agent-to-agent messaging) excel at reliability and task delegation, but they fall short in enabling emergent, swarm-like intelligence, where distributed agents continuously learn, adapt, and communicate to form collective cognition. This paper revisits gossip protocols, long valued in distributed systems for their fault tolerance and decentralization, and argues that they offer a missing layer for context-rich, adaptive communication in agentic AI. Gossip enables scalable, low-overhead dissemination of shared knowledge, but also raises unresolved challenges around semantic filtering, staleness, trustworthiness, and consistency in high-stakes environments. Rather than proposing a new framework, this work charts a research agenda for integrating gossip as a complementary substrate alongside structured protocols. We identify critical gaps in current agent-to-agent architectures, highlight where gossip could reshape assumptions about coordination, and outline open questions around intent propagation, knowledge decay, and peer-to-peer trust. Gossip is not a silver bullet, but overlooking it risks missing a key path toward resilient, reflexive, and self-organizing multi-agent systems.