Trajectory prediction is a key element of autonomous vehicle systems, enabling them to anticipate and react to the movements of other road users. Evaluating the robustness of prediction models against adversarial attacks is essential to ensure their reliability in real-world traffic. However, current approaches tend to focus on perturbing the past positions of surrounding agents, which can generate unrealistic scenarios and overlook critical vulnerabilities. This limitation may result in overly optimistic assessments of model performance in real-world conditions. In this work, we demonstrate that perturbing not just past but also future states of adversarial agents can uncover previously undetected weaknesses and thereby provide a more rigorous evaluation of model robustness. Our novel approach incorporates dynamic constraints and preserves tactical behaviors, enabling more effective and realistic adversarial attacks. We introduce new performance measures to assess the realism and impact of these adversarial trajectories. Testing our method on a state-of-the-art prediction model revealed significant increases in prediction errors and collision rates under adversarial conditions. Qualitative analysis further showed that our attacks can expose critical weaknesses, such as the inability of the model to detect potential collisions in what appear to be safe predictions. These results underscore the need for more comprehensive adversarial testing to better evaluate and improve the reliability of trajectory prediction models for autonomous vehicles.

In-X Subnetworks are envisioned to meet the stringent demands of short-range communication in diverse 6G use cases. In the context of In-Factory scenarios, effective power control is critical to mitigating the impact of interference resulting from potentially high subnetwork density. Existing approaches to power control in this domain have predominantly emphasized the data plane, often overlooking the impact of signaling overhead. Furthermore, prior work has typically adopted a network-centric perspective, relying on the assumption of complete and up-to-date channel state information (CSI) being readily available at the central controller. This paper introduces a novel multi-agent reinforcement learning (MARL) framework designed to enable access points to autonomously learn both signaling and power control protocols in an In-Factory Subnetwork environment. By formulating the problem as a partially observable Markov decision process (POMDP) and leveraging multi-agent proximal policy optimization (MAPPO), the proposed approach achieves significant advantages. The simulation results demonstrate that the learning-based method reduces signaling overhead by a factor of 8 while maintaining a buffer flush rate that lags the ideal "Genie" approach by only 5%.

We propose a model for the evolutionary ecology of words as one attempt to extend evolutionary game theory and agent-based models by utilizing the rich linguistic expressions of Large Language Models (LLMs). Our model enables the emergence and evolution of diverse and infinite options for interactions among agents. Within the population, each agent possesses a short word (or phrase) generated by an LLM and moves within a spatial environment. When agents become adjacent, the outcome of their interaction is determined by the LLM based on the relationship between their words, with the loser's word being replaced by the winner's. Word mutations, also based on LLM outputs, may occur. We conducted preliminary experiments assuming that ``strong animal species" would survive. The results showed that from an initial population consisting of well-known species, many species emerged both gradually and in a punctuated equilibrium manner. Each trial demonstrated the unique evolution of diverse populations, with one type of large species becoming dominant, such as terrestrial animals, marine life, or extinct species, which were ecologically specialized and adapted ones across diverse extreme habitats. We also conducted a long-term experiment with a large population, demonstrating the emergence and coexistence of diverse species.

This paper presents a unified approach to realize versatile distributed maneuvering with generalized formations. Specifically, we decompose the robots' maneuvers into two independent components, i.e., interception and enclosing, which are parameterized by two independent virtual coordinates. Treating these two virtual coordinates as dimensions of an abstract manifold, we derive the corresponding singularity-free guiding vector field (GVF), which, along with a distributed coordination mechanism based on the consensus theory, guides robots to achieve various motions (i.e., versatile maneuvering), including (a) formation tracking, (b) target enclosing, and (c) circumnavigation. Additional motion parameters can generate more complex cooperative robot motions. Based on GVFs, we design a controller for a nonholonomic robot model. Besides the theoretical results, extensive simulations and experiments are performed to validate the effectiveness of the approach.

The study investigates the coffee value chain dynamics in Davao del Sur using an agent-based model. Three main factors driving interactions among key players were identified: trust, risk, and transaction costs. The model was constructed using NetLogo 6.3.0, and data from a survey questionnaire collected three data points from BACOFA members. Five cases were explored, with each scenario simulated 1000 times. Findings suggest that producers often sell to the market rather than the cooperative due to higher prices. However, producers tend to prioritize trust in buyers and their risk attitude, leading to increased sales to the cooperative. The producer's risk attitude significantly influences their decision-making, affecting performance outcomes such as loans, demand, and price changes. All three factors play a role and exert varying impacts on the value chain. So, the stakeholders' decisions on prioritizing factors in improving relationships depend on their priorities. Nonetheless, simulations show that establishing a harmonious system benefiting all parties is possible. However, achieving this requires adjustments to demand, pricing, trust, and risk attitudes of key players, which may not align with the preferences of some parties in reality.

-- This paper proposes a novel formation maneuver control method for both 2-D and 3-D space, which enables the formation to translate, scale, and rotate with arbitrary orientation. The core innovation is the novel design of weights in the proposed augmented Laplacian matrix. Instead of using scalars, we represent weights as matrices, which are designed based on a specified rotation axis and allow the formation to perform rotation in 3-D space. T o further improve the flexibility and scalability of the formation, the rotational axis adjustment approach and dynamic agent reconfiguration method are developed, allowing formations to rotate around arbitrary axes in 3-D space and new agents to join the formation. Theoretical analysis is provided to show that the proposed approach preserves the original configuration of the formation. The proposed method maintains the advantages of the complex Laplacian-based method, including reduced neighbor requirements and no reliance on generic or convex nominal configurations, while achieving arbitrary orientation rotations via a more simplified implementation. Simulations in both 2-D and 3-D space validate the effectiveness of the proposed method. In recent years, formation control of multi-agent systems has gained significant attention due to its wide range of applications in various fields, such as drone swarms [1], AUV formations [2], robotic cooperation [3], etc.

The internet is undergoing a historical transformation from the "Internet of Websites" to the "Internet of AgentSites." While traditional Websites served as the foundation for information hosting and dissemination, a new frontier is emerging where AgentSites serve as the hubs of the internet, where each AgentSite hosts one or more AI agents that receive tasks, address them, and deliver actionable solutions, marking a significant shift in the digital landscape and representing the next generation of online ecosystems. Under this vision, AIOS, the AI Agent Operating System, serves as the server for the development, deployment and execution of AI agents, which is a fundamental infrastructure for the Internet of Agentsites. In this paper, we introduce AIOS Server, a runtime framework to host agents and enable global-scale collaboration among decentralized agents. AIOS Server provides a communication protocol leveraging the Model Context Protocol (MCP) and JSON-RPC to enable agent-agent or human-agent interactions. Each AIOS node operates as a server to host and execute agents, while supporting peer-to-peer coordination without reliance on centralized orchestration. Based on AIOS Server, we further present the world's first practically deployed Internet of Agentsites (AIOS-IoA), including AgentHub for agent registration and discovery and AgentChat for interactive communication, at https://planet.aios.foundation. The agent discovery mechanism based on Distributed Hash Tables (DHT) and a Gossip protocol serves as the search engine for the internet of agentsites. This work provides a practical foundation for building the Internet of Agentsites-a new paradigm where autonomous agents become first-class citizens of the web. The implementation is available at https://github.com/agiresearch/AIOS.Server and is integrated into the AIOS main branch at https://github.com/agiresearch/AIOS.

While the use of MLdriven systems can enhance efficiency, it can also drive the humans who are subject to algorithmic decisions to adjust their behavior accordingly. Examples include Uber drivers coordinating their behavior in response to its surge pricing algorithm [Möhlmann and Zalmanson, 2017], applicants selecting keywords and formatting to pass automated resume screening [Forbes, 2022], and Facebook users adjusting their posting and content interaction choices in response to the platforms' curation algorithms [Eslami et al., 2016]. These can be viewed as strategic responses by rational human subjects in these systems, motivating a game-theoretical analysis of learning algorithms with human in the loop. Earlier works on the study of strategic humans facing ML systems largely focused on scenarios where users can strategically alter only their observable data (e.g., students cheating to obtain better test scores, job applicants making formatting or wording changes to their CV, or loan applicants opening several new accounts to increase their credit scores) to receive a favorable decision (e.g., be accepted to a school, job opening, or loan); see, e.g., [Hu et al., 2019, Milli et al., 2019]. This strategic behavior is referred to as strategic manipulation, where agents change their features without changing their true qualification states. This can be interpreted as cheating the machine learning algorithm: such agents may appear to be more qualified, without being truly suitable for a favorable outcome.

Trust is a fundamental component of human-agent interaction. With the increasing presence of artificial agents in daily life, it is essential to understand how people perceive and trust these agents. One of the key challenges affecting this perception is the Uncanny Valley Effect (UVE), where increasingly human-like artificial beings can be perceived as eerie or repelling. Despite growing interest in trust and the UVE, existing research varies widely in terms of how these concepts are defined and operationalized. This inconsistency raises important questions about how and under what conditions the UVE influences trust in agents. A systematic understanding of their relationship is currently lacking. This review aims to examine the impact of the UVE on human trust in agents and to identify methodological patterns, limitations, and gaps in the existing empirical literature. Following PRISMA guidelines, a systematic search identified 53 empirical studies that investigated both UVE-related constructs and trust or trust-related outcomes. Studies were analyzed based on a structured set of categories, including types of agents and interactions, methodological and measurement approaches, and key findings. The results of our systematic review reveal that most studies rely on static images or hypothetical scenarios with limited real-time interaction, and the majority use subjective trust measures. This review offers a novel framework for classifying trust measurement approaches with regard to the best-practice criteria for empirically investigating the UVE. As the first systematic attempt to map the intersection of UVE and trust, this review contributes to a deeper understanding of their interplay and offers a foundation for future research. Keywords: the uncanny valley effect, trust, human-likeness, affinity response, human-agent interaction

As frontier AI systems advance toward transformative capabilities, we need a parallel transformation in how we measure and evaluate these systems to ensure safety and inform governance. While benchmarks have been the primary method for estimating model capabilities, they often fail to establish true upper bounds or predict deployment behavior. This literature review consolidates the rapidly evolving field of AI safety evaluations, proposing a systematic taxonomy around three dimensions: what properties we measure, how we measure them, and how these measurements integrate into frameworks. We show how evaluations go beyond benchmarks by measuring what models can do when pushed to the limit (capabilities), the behavioral tendencies exhibited by default (propensities), and whether our safety measures remain effective even when faced with subversive adversarial AI (control). These properties are measured through behavioral techniques like scaffolding, red teaming and supervised fine-tuning, alongside internal techniques such as representation analysis and mechanistic interpretability. We provide deeper explanations of some safety-critical capabilities like cybersecurity exploitation, deception, autonomous replication, and situational awareness, alongside concerning propensities like power-seeking and scheming. The review explores how these evaluation methods integrate into governance frameworks to translate results into concrete development decisions. We also highlight challenges to safety evaluations - proving absence of capabilities, potential model sandbagging, and incentives for "safetywashing" - while identifying promising research directions. By synthesizing scattered resources, this literature review aims to provide a central reference point for understanding AI safety evaluations.