Future 6G radio access networks (RANs) will be artificial intelligence (AI)-native: observed, reasoned about, and re-configured by autonomous agents cooperating across the cloud-edge continuum. We introduce MX-AI, the first end-to-end agentic system that (i) instruments a live 5G Open RAN testbed based on OpenAirInterface (OAI) and FlexRIC, (ii) deploys a graph of Large-Language-Model (LLM)-powered agents inside the Service Management and Orchestration (SMO) layer, and (iii) exposes both observability and control functions for 6G RAN resources through natural-language intents. On 50 realistic operational queries, MX-AI attains a mean answer quality of 4.1/5.0 and 100 % decision-action accuracy, while incurring only 8.8 seconds end-to-end latency when backed by GPT-4.1. Thus, it matches human-expert performance, validating its practicality in real settings. We publicly release the agent graph, prompts, and evaluation harness to accelerate open research on AI-native RANs. A live demo is presented here: https://www.youtube.com/watch?v=CEIya7988Ug&t=285s&ab_channel=BubbleRAN

Current AI agents create significant barriers for users by requiring extensive processing to understand web pages, making AI-assisted web interaction slow and expensive. This paper introduces webMCP (Web Machine Context & Procedure), a client-side standard that embeds structured interaction metadata directly into web pages, enabling more efficient human-AI collaboration on existing websites. webMCP transforms how AI agents understand web interfaces by providing explicit mappings between page elements and user actions. Instead of processing entire HTML documents, agents can access pre-structured interaction data, dramatically reducing computational overhead while maintaining task accuracy. A comprehensive evaluation across 1,890 real API calls spanning online shopping, authentication, and content management scenarios demonstrates webMCP reduces processing requirements by 67.6% while maintaining 97.9% task success rates compared to 98.8% for traditional approaches. Users experience significantly lower costs (34-63% reduction) and faster response times across diverse web interactions. Statistical analysis confirms these improvements are highly significant across multiple AI models. An independent WordPress deployment study validates practical applicability, showing consistent improvements across real-world content management workflows. webMCP requires no server-side modifications, making it deployable across millions of existing websites without technical barriers. These results establish webMCP as a viable solution for making AI web assistance more accessible and sustainable, addressing the critical gap between user interaction needs and AI computational requirements in production environments.

Collective behaviors such as swarming and flocking emerge from simple, decentralized interactions in biological systems. Existing models, such as Vicsek and Cucker-Smale, lack collision avoidance, whereas the Olfati-Saber model imposes rigid formations, limiting their applicability in swarm robotics. To address these limitations, this paper proposes a minimal yet expressive model that governs agent dynamics using relative positions, velocities, and local density, modulated by two tunable parameters: the spatial offset and kinetic offset. The model achieves spatially flexible, collision-free behaviors that reflect naturalistic group dynamics. Furthermore, we extend the framework to cognitive autonomous systems, enabling energy-aware phase transitions between swarming and flocking through adaptive control parameter tuning. This cognitively inspired approach offers a robust foundation for real-world applications in multi-robot systems, particularly autonomous aerial swarms.

MicroRNAs (miRs) are robust regulators of gene expression, implicated in most biological processes. microRNAs predominantly downregulate the expression of genes post-transcriptionally and each miR is predicted to target several hundred genes. The accurate identification and annotation of miR-mRNA target interactions is central to understanding miRs function and their therapeutic potential. However, computational target prediction is challenging due to imperfect complementarity of miRs with their targets and the growing volume and heterogeneity of experimental data present challenges in accessing, integrating, and analysing miR-target interaction information across biological contexts. This creates a need for integrated resources and intelligent query tools. We present the miRKat Suite, comprising miRKatDB, a comprehensive, curated database of predicted and validated miR-target interactions and associated annotations, and miRKatAI, a multi-agent system powered by large language models (LLMs) and LangGraph. miRKatDB integrates data from multiple publicly available sources, providing a comprehensive foundation for miR studies, including miR target genes and changes in levels of tissue expression previously reported. miRKatAI offers a natural language interface for complex querying of miRKatDB, facilitates grounded information retrieval from established sources in the field, and supports basic data visualisation. The miRKat Suite aims to accelerate miR research by streamlining data access, enhancing exploratory analysis, and supporting hypothesis generation.

Hegselmann--Krause models are localized, distributed averaging dynamics on spatial data. A key aspect of these dynamics is that they lead to cluster formation, which has important applications in geographic information systems, dynamic clustering algorithms, opinion dynamics, and social networks. For these models, the key questions are whether a fixed point exists and, if so, characterizing it. In this work, we establish new results towards the "2R-Conjecture" for the Hegselmann--Krause model, for which no meaningful progress, or even any precise statement, has been made since its introduction in 2007. This conjecture relates to the structure of the fixed point when there are a large number of agents per unit space. We provide, among other results, a proof in expectation and a statement of a stronger result that is supported by simulation. The key methodological contribution is to consider the dynamics as an infinite-dimensional problem on the space of point processes, rather than on finitely many points. This enables us to leverage stationarity, shift invariance, and certain other symmetries to obtain the results. These techniques do not have finite-dimensional analogs.

Recent advancements in embodied AI and robotic manipulation have highlighted the need for scalable, interactive simulation environments that support both scene construction and data collection for training autonomous agents. While existing platforms [1, 2, 3, 4] offer partial solutions, such as scene generation[5, 3, 4] or task-specific manipulation datasets [6, 7, 8], few provide a unified framework that integrates high-fidelity scene construction with flexible mobile robotic data collection system. To bridge this gap, we present AgentWorld, an interactive simulation platform designed for procedural scene construction and mobile-based teleoperation, enabling efficient data collection for imitation learning in complex household environments. AgentWorld addresses two critical challenges in embodied AI research: (1) stable and diverse scene generation, ensuring that the simulated environments are visually realistic and physically plausible, and (2) a comprehensive data collection system in simulation, which allows seamless control of mobile bases and robotic arms for data collection. AgentWorld is built upon NVIDIA's Omniverse Isaac Sim [9] and Unreal Engine [10], allowing it to inherit both strengths including the physics engine for robot parallel training and realistic rendering effects.

Recent progress in large language models (LLMs) has enabled the development of autonomous web agents capable of navigating and interacting with real websites. However, evaluating such agents remains challenging due to the instability and inconsistency of existing benchmarks, which often rely on dynamic content or oversimplified simulations. In this work, we introduce WebArXiv, a static and time-invariant benchmark comprising 275 web-based tasks grounded in the arXiv platform. WebArXiv ensures reproducible and reliable evaluation by anchoring tasks in fixed web snapshots with deterministic ground truths and standardized action trajectories. Through behavioral analysis, we identify a common failure mode, Rigid History Reflection, where agents over-rely on fixed interaction histories. To address this, we propose a lightweight dynamic reflection mechanism that allows agents to selectively retrieve relevant past steps during decision-making. We evaluate ten state-of-the-art web agents on WebArXiv. Results demonstrate clear performance differences across agents and validate the effectiveness of our proposed reflection strategy.

Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a wide range of applications in practice. To date, however, most theoretical convergence studies for existing actor-critic decentralized MARL methods are limited to the guarantee of a stationary solution under the linear function approximation. This leaves a significant gap between the highly successful use of deep neural actor-critic for decentralized MARL in practice and the current theoretical understanding. To bridge this gap, in this paper, we make the first attempt to develop a deep neural actor-critic method for decentralized MARL, where both the actor and critic components are inherently non-linear. We show that our proposed method enjoys a global optimality guarantee with a finite-time convergence rate of O(1/T), where T is the total iteration times. This marks the first global convergence result for deep neural actor-critic methods in the MARL literature. We also conduct extensive numerical experiments, which verify our theoretical results.

Automated parking is a critical feature of Advanced Driver Assistance Systems (ADAS), where accurate trajectory prediction is essential to bridge perception and planning modules. Despite its significance, research in this domain remains relatively limited, with most existing studies concentrating on single-modal trajectory prediction of vehicles. In this work, we propose ParkDiffusion, a novel approach that predicts the trajectories of both vehicles and pedestrians in automated parking scenarios. ParkDiffusion employs diffusion models to capture the inherent uncertainty and multi-modality of future trajectories, incorporating several key innovations. First, we propose a dual map encoder that processes soft semantic cues and hard geometric constraints using a two-step cross-attention mechanism. Second, we introduce an adaptive agent type embedding module, which dynamically conditions the prediction process on the distinct characteristics of vehicles and pedestrians. Third, to ensure kinematic feasibility, our model outputs control signals that are subsequently used within a kinematic framework to generate physically feasible trajectories. We evaluate ParkDiffusion on the Dragon Lake Parking (DLP) dataset and the Intersections Drone (inD) dataset. Our work establishes a new baseline for heterogeneous trajectory prediction in parking scenarios, outperforming existing methods by a considerable margin.

-- Multi-vehicle trajectory planning (MVTP) is one of the key challenges in multi-robot systems (MRSs) and has broad applications across various fields. This paper presents ESCoT, an enhanced step-based coordinate trajectory planning method for multiple car-like robots. ESCoT incorporates two key strategies: collaborative planning for local robot groups and replanning for duplicate configurations. These strategies effectively enhance the performance of step-based MVTP methods. Through extensive experiments, we show that ESCoT 1) in sparse scenarios, significantly improves solution quality compared to baseline step-based method, achieving up to 70% improvement in typical conflict scenarios and 34% in randomly generated scenarios, while maintaining high solving efficiency; and 2) in dense scenarios, outperforms all baseline methods, maintains a success rate of over 50% even in the most challenging configurations. The results demonstrate that ESCoT effectively solves MVTP, further extending the capabilities of step-based methods. Research on multi-robot systems (MRSs) has attracted growing attention in recent years, for its capability of accomplishing complicated missions more effectively and efficiently [1], [2]. MRSs involve many critical problems, one of which is multi-agent path finding (MAPF). MAPF focuses on finding conflict-free paths for multiple agents in a shared environment, ensuring that each agent can navigate from its start position to its goal without colliding with others [3].