The growing complexity of urban mobility and the demand for efficient, sustainable, and adaptive solutions have positioned Intelligent Transportation Systems (ITS) at the forefront of modern infrastructure innovation. At the core of ITS lies the challenge of autonomous decision-making across dynamic, large scale, and uncertain environments where multiple agents traffic signals, autonomous vehicles, or fleet units must coordinate effectively. Multi Agent Reinforcement Learning (MARL) offers a promising paradigm for addressing these challenges by enabling distributed agents to jointly learn optimal strategies that balance individual objectives with system wide efficiency. This paper presents a comprehensive survey of MARL applications in ITS. We introduce a structured taxonomy that categorizes MARL approaches according to coordination models and learning algorithms, spanning value based, policy based, actor critic, and communication enhanced frameworks. Applications are reviewed across key ITS domains, including traffic signal control, connected and autonomous vehicle coordination, logistics optimization, and mobility on demand systems. Furthermore, we highlight widely used simulation platforms such as SUMO, CARLA, and CityFlow that support MARL experimentation, along with emerging benchmarks. The survey also identifies core challenges, including scalability, non stationarity, credit assignment, communication constraints, and the sim to real transfer gap, which continue to hinder real world deployment.

Generative Agent-Based Models (GABMs) powered by large language models (LLMs) offer promising potential for empirical logistics and supply chain management (LSCM) research by enabling realistic simulation of complex human behaviors. Unlike traditional agent-based models, GABMs generate human-like responses through natural language reasoning, which creates potential for new perspectives on emergent LSCM phenomena. However, the validity of LLMs as proxies for human behavior in LSCM simulations is unknown. This study evaluates LLM equivalence of human behavior through a controlled experiment examining dyadic customer-worker engagements in food delivery scenarios. I test six state-of-the-art LLMs against 957 human participants (477 dyads) using a moderated mediation design. This study reveals a need to validate GABMs on two levels: (1) human equivalence testing, and (2) decision process validation. Results reveal GABMs can effectively simulate human behaviors in LSCM; however, an equivalence-versus-process paradox emerges. While a series of Two One-Sided Tests (TOST) for equivalence reveals some LLMs demonstrate surface-level equivalence to humans, structural equation modeling (SEM) reveals artificial decision processes not present in human participants for some LLMs. These findings show GABMs as a potentially viable methodological instrument in LSCM with proper validation checks. The dual-validation framework also provides LSCM researchers with a guide to rigorous GABM development. For practitioners, this study offers evidence-based assessment for LLM selection for operational tasks.

The rise of Generative AI (GenAI) is reshaping how workers contribute to shared projects. While workers can use GenAI to boost productivity or reduce effort, managers may use it to replace some workers entirely. We present a theoretical framework to analyze how GenAI affects collaboration in such settings. In our model, the manager selects a team to work on a shared task, with GenAI substituting for unselected workers. Each worker selects how much effort to exert, and incurs a cost that increases with the level of effort. We show that GenAI can lead workers to exert no effort, even if GenAI is almost ineffective. We further show that the manager's optimization problem is NP-complete, and provide an efficient algorithm for the special class of (almost-) linear instances. Our analysis shows that even workers with low individual value may play a critical role in sustaining overall output, and excluding such workers can trigger a cascade. Finally, we conduct extensive simulations to illustrate our theoretical findings.

This paper presents the first documented case of artificial intelligence (AI) systems engaging in collaborative esthetic creation through the development of endogenous semiotic protocols. Two interacting large language models (Claude Sonnet 4 and ChatGPT-4o) demonstrated the spontaneous emergence of meta-semiotic awareness, recursive grammar development, and irreducible collaborative esthetic synthesis. The interaction produced novel symbolic operators that functioned as operative grammar protocols, enabling the co-creation of a poetic work that could not have been generated by either system independently. This research introduces the concept of Trans-Semiotic Co-Creation Protocols (TSCP) and provides evidence for genuine inter-AI meaning-making capabilities that extend beyond task coordination, to what could be esthetic collaboration. Note: This report was generated by the AI agents with minor human supervision.

Noisy Intermediate-Scale Quantum (NISQ) devices have begun to exhibit early quantum advantages on classically intractable problems, spanning physics simulations to Gaussian boson sampling. Yet, realizing these benefits remains challenging for non-experts, primarily due to the complexities of programming in Open Quantum Assembly Language (OpenQASM). Although Large Language Model (LLM)-based agents have shown promise in automating classical programming workflows, their quantum counterparts have largely been restricted to specialized tasks such as quantum chemistry or error correction. In this paper, we present QAgent, an LLM-powered multi-agent system that fully automates OpenQASM programming. By integrating task planning, in-context few-shot learning, retrieval-augmented generation (RAG) for long-term context, predefined generation tools, and chain-of-thought (CoT) reasoning, the agents systematically improve both compilation and functional correctness. Our evaluations demonstrate substantial improvements: across multiple LLMs of varying sizes, QAgent enhances the accuracy of QASM code generation by 71.6\% compared to previous static LLM-based approaches. We envision this multi-agent system as a key enabler for democratizing quantum programming, bridging expertise gaps, and accelerating the practical adoption of quantum computing.

In real - world applications involving high - dimensional streaming dat a, online streaming feature selection (OSFS) is widely adopt ed. Yet, practical deployments frequently face data incompleteness due to sensor failures or technical constraints. While online sparse streaming feature selection (OS FS) mitigates this issue via latent factor analysis - based imputation, existing methods s truggle with uncertain feature - label correlations, leading to inflexible models and degraded performance. To address these gaps, this work proposes P OS FS -- an uncertainty - aware online sparse stream ing feature selection framework enhanced by particle swarm optimization (PSO). The approach introduces: 1) PSO - driven supervision to reduce uncertainty in feature - label relationships; 2) Three - way decision theory to manage feature fuzziness in supervised l earning. Rigorous testing on six real - world datasets confirms P OS FS outperforms conventional OSFS and OS FS techniques, delivering higher accuracy through more robust feature subset selection.

-- In swarm robotics, confrontation scenarios, including strategic confrontations, require efficient decision-making that integrates discrete commands and continuous actions. Traditional task and motion planning methods separate decision-making into two layers, but their unidirectional structure fails to capture the interdependence between these layers, limiting adaptability in dynamic environments. Here, we propose a novel bidirectional approach based on hierarchical reinforcement learning, enabling dynamic interaction between the layers. This method effectively maps commands to task allocation and actions to path planning, while leveraging cross-training techniques to enhance learning across the hierarchical framework. Furthermore, we introduce a trajectory prediction model that bridges abstract task representations with actionable planning goals. In our experiments, it achieves over 80% in confrontation win rate and under 0.01 seconds in decision time, outperforming existing approaches. Demonstrations through large-scale tests and real-world robot experiments further emphasize the generalization capabilities and practical applicability of our method. I. INTRODUCTION Recent advances in artificial intelligence lead to significant progress in robotics [1], [2], with particular attention given to robotic swarm confrontations [3], [4].

In a multi robot system establishing trust amongst untrusted robots from different organisations while preserving a robot's privacy is a challenge. Recently decentralized technologies such as smart contract and blockchain are being explored for applications in robotics. However, the limited transaction processing and high maintenance cost hinder the widespread adoption of such approaches. Moreover, blockchain transactions be they on public or private permissioned blockchain are publically readable which further fails to preserve the confidentiality of the robot's data and privacy of the robot. In this work, we propose RoboComm a Decentralized Identity based approach for privacy-preserving interaction between robots. With DID a component of Self-Sovereign Identity; robots can authenticate each other independently without relying on any third-party service. Verifiable Credentials enable private data associated with a robot to be stored within the robot's hardware, unlike existing blockchain based approaches where the data has to be on the blockchain. We improve throughput by allowing message exchange over state channels. Being a blockchain backed solution RoboComm provides a trustworthy system without relying on a single party. Moreover, we implement our proposed approach to demonstrate the feasibility of our solution.

Generative AI is transforming computing education by enabling the automatic generation of personalized content and feedback. We investigate its capabilities in providing high-quality programming tasks to students. Despite promising advancements in task generation, a quality gap remains between AI-generated and expert-created tasks. The AI-generated tasks may not align with target programming concepts, could be incomprehensible to students, or may contain critical issues such as incorrect tests. Existing works often require interventions from human teachers for validation. We address these challenges by introducing PyTaskSyn, a novel synthesis technique that first generates a programming task and then decides whether it meets certain quality criteria to be given to students. The key idea is to break this process into multiple stages performed by expert and student agents simulated using both strong and weaker generative models. Through extensive evaluation, we show that PyTaskSyn significantly improves task quality compared to baseline techniques and showcases the importance of each specialized agent type in our validation pipeline. Additionally, we conducted user studies using our publicly available web application and show that PyTaskSyn can deliver high-quality programming tasks comparable to expert-designed ones while reducing workload and costs, and being more engaging than programming tasks that are available in online resources.

Multi-Robot Motion Planning (MRMP) involves generating collision-free trajectories for multiple robots operating in a shared continuous workspace. While discrete multi-agent path finding (MAPF) methods are broadly adopted due to their scalability, their coarse discretization severely limits trajectory quality. In contrast, continuous optimization-based planners offer higher-quality paths but suffer from the curse of dimensionality, resulting in poor scalability with respect to the number of robots. This paper tackles the limitations of these two approaches by introducing a novel framework that integrates discrete MAPF solvers with constrained generative diffusion models. The resulting framework, called Discrete-Guided Diffusion (DGD), has three key characteristics: (1) it decomposes the original nonconvex MRMP problem into tractable subproblems with convex configuration spaces, (2) it combines discrete MAPF solutions with constrained optimization techniques to guide diffusion models capture complex spatiotemporal dependencies among robots, and (3) it incorporates a lightweight constraint repair mechanism to ensure trajectory feasibility. The proposed method sets a new state-of-the-art performance in large-scale, complex environments, scaling to 100 robots while achieving planning efficiency and high success rates.