The second paradigm involves LLM agents developed using frameworks like LangChain [16], AutoGPT [11], Langroid [18], AutoGen [23], and LlamaIndex [22], which offer greater programmability and modularity, allowing developers to build sophisticated, multi-agent systems that integrate external tools and dynamic workflows [20]. Despite their advantages, both paradigms remain architecturally fragmented and lack standardized interoperability, leading to redundant development efforts and constrained scalability. From a software engineering (SE) perspective, current LLM application paradigms resemble traditional platform-centric software ecosystems, where applications are tightly coupled to proprietary APIs and execution environments. LLM app stores, while lowering the barrier to entry, impose constraints on extensibility and cross-platform interoperability, leading to vendor lock-in and duplicated development efforts across different ecosystems. In contrast, agent-based LLM frameworks provide modularity but lack standardized mechanisms for component reuse and integration, making it challenging to compose LLM applications that seamlessly operate across heterogeneous environments. This fragmentation mirrors historical challenges in SE, where monolithic architectures have given way to service-oriented and microservices-based designs to improve reusability, scalability, and maintainability. Another key limitation of existing LLM applications is inefficient hardware utilization.

In modern product development, Computer Aided Design and Engineering (CAD/E) plays a key role to turn innovative ideas and visions into tangible and manufacturable designs. Digital 2D and 3D geometry representations of objects on different levels of granularity are required in various intermediate development steps, for example aesthetic discussions, design quality evaluations based on simulation tools, and design feasibility checks. For these steps, development teams include various roles such as requirement engineers, style designers, Computer-Aided Design (CAD) experts, simulation domain experts, and quality assurance teams who create a product cooperatively. Stakeholders in these roles utilize software tools to implement digital representations of products, also referred to as digital twins. This process receives an increasing amount of support in the form of Artificial Intelligence (AI) methods. For example, data science methods provide efficient ways to improve the problem understanding, e.g., by calculating design sensitivities towards a certain performance aspect [Gräning and Sendhoff, 2014], or displaying the distribution of design variations in the solution space using clustering [Lanfermann et al., 2020].

Large Language Model based multi-agent systems are revolutionizing autonomous communication and collaboration, yet they remain vulnerable to security threats like unauthorized access and data breaches. To address this, we introduce AgentSafe, a novel framework that enhances MAS security through hierarchical information management and memory protection. AgentSafe classifies information by security levels, restricting sensitive data access to authorized agents. AgentSafe incorporates two components: ThreatSieve, which secures communication by verifying information authority and preventing impersonation, and HierarCache, an adaptive memory management system that defends against unauthorized access and malicious poisoning, representing the first systematic defense for agent memory. Experiments across various LLMs show that AgentSafe significantly boosts system resilience, achieving defense success rates above 80% under adversarial conditions. Additionally, AgentSafe demonstrates scalability, maintaining robust performance as agent numbers and information complexity grow. Results underscore effectiveness of AgentSafe in securing MAS and its potential for real-world application.

Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL) offer promising solutions for complex, dynamic environments where decision-making entities must interact and adapt. In cybersecurity, particularly in Automated Cyber Defence(ACD), these techniques can address challenges posed by high-dimensional observations and actions. This document provides guidelines for: Cybersecurity professionals exploring RL and MARL for real-world applications. RL and MARL researchers aiming to tackle the nuanced demands of cybersecurity scenarios. By outlining when RL and MARL are appropriate, addressing cyber-specific challenges, and offering practical considerations for implementation, these guidelines aim to bridge the gap between theoretical research and practical deployment in adversarial settings. We expect that this document will offer support to researchers who are keen to explore topics at the intersection of RL, MARL and ACD by highlighting open research questions and topics that demand further investigation.

Online learning algorithms are widely used in strategic multi-agent settings, including repeated auctions, contract design, and pricing competitions, where agents adapt their strategies over time. A key question in such environments is how an optimizing agent can best respond to a learning agent to improve its own long-term outcomes. While prior work has developed efficient algorithms for the optimizer in special cases - such as structured auction settings or contract design - no general efficient algorithm is known. In this paper, we establish a strong computational hardness result: unless $\mathsf{P} = \mathsf{NP}$, no polynomial-time optimizer can compute a near-optimal strategy against a learner using a standard no-regret algorithm, specifically Multiplicative Weights Update (MWU). Our result proves an $\Omega(T)$ hardness bound, significantly strengthening previous work that only showed an additive $\Theta(1)$ impossibility result. Furthermore, while the prior hardness result focused on learners using fictitious play - an algorithm that is not no-regret - we prove intractability for a widely used no-regret learning algorithm. This establishes a fundamental computational barrier to finding optimal strategies in general game-theoretic settings.

Temporal progression is an integral part of knowledge accumulation and update. Web search is frequently adopted as grounding for agent knowledge, yet its inappropriate configuration affects the quality of agent responses. Here, we construct a tool-based out-of-sample testing framework to measure the knowledge variability of large language model (LLM) agents from distinct date-controlled tools (DCTs). We demonstrate the temporal effects of an LLM agent as a writing assistant, which can use web search to help complete scientific publication abstracts. We show that temporal effects of the search engine translates into tool-dependent agent performance but can be alleviated with base model choice and explicit reasoning instructions such as chain-of-thought prompting. Our results indicate that agent evaluation should take a dynamical view and account for the temporal influence of tools and the updates of external resources.

Cooperative Simultaneous Localization and Mapping (C-SLAM) enables multiple agents to work together in mapping unknown environments while simultaneously estimating their own positions. This approach enhances robustness, scalability, and accuracy by sharing information between agents, reducing drift, and enabling collective exploration of larger areas. In this paper, we present Decentralized Visual Monocular SLAM (DVM-SLAM), the first open-source decentralized monocular C-SLAM system. By only utilizing low-cost and light-weight monocular vision sensors, our system is well suited for small robots and micro aerial vehicles (MAVs). DVM-SLAM's real-world applicability is validated on physical robots with a custom collision avoidance framework, showcasing its potential in real-time multi-agent autonomous navigation scenarios. We also demonstrate comparable accuracy to state-of-the-art centralized monocular C-SLAM systems. We open-source our code and provide supplementary material online.

Agents based on large language models (LLMs) have demonstrated strong capabilities in a wide range of complex, real-world applications. However, LLM agents with a compromised memory bank may easily produce harmful outputs when the past records retrieved for demonstration are malicious. In this paper, we propose a novel Memory INJection Attack, MINJA, that enables the injection of malicious records into the memory bank by only interacting with the agent via queries and output observations. These malicious records are designed to elicit a sequence of malicious reasoning steps leading to undesirable agent actions when executing the victim user's query. Specifically, we introduce a sequence of bridging steps to link the victim query to the malicious reasoning steps. During the injection of the malicious record, we propose an indication prompt to guide the agent to autonomously generate our designed bridging steps. We also propose a progressive shortening strategy that gradually removes the indication prompt, such that the malicious record will be easily retrieved when processing the victim query comes after. Our extensive experiments across diverse agents demonstrate the effectiveness of MINJA in compromising agent memory. With minimal requirements for execution, MINJA enables any user to influence agent memory, highlighting practical risks of LLM agents.

Multi-agent systems have emerged as a promising approach for enhancing the reasoning capabilities of large language models in complex problem-solving. However, current MAS frameworks are limited by poor flexibility and scalability, with underdeveloped optimization strategies. To address these challenges, we propose ReSo, which integrates task graph generation with a reward-driven two-stage agent selection process. The core of ReSo is the proposed Collaborative Reward Model, which can provide fine-grained reward signals for MAS cooperation for optimization. We also introduce an automated data synthesis framework for generating MAS benchmarks, without human annotations. Experimentally, ReSo matches or outperforms existing methods. ReSo achieves \textbf{33.7\%} and \textbf{32.3\%} accuracy on Math-MAS and SciBench-MAS SciBench, while other methods completely fail. Code is available at: \href{https://github.com/hengzzzhou/ReSo}{ReSo}

Designing effective reward functions in multi-agent reinforcement learning (MARL) is a significant challenge, often leading to suboptimal or misaligned behaviors in complex, coordinated environments. We introduce Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality (M3HF), a novel framework that integrates multi-phase human feedback of mixed quality into the MARL training process. By involving humans with diverse expertise levels to provide iterative guidance, M3HF leverages both expert and non-expert feedback to continuously refine agents' policies. During training, we strategically pause agent learning for human evaluation, parse feedback using large language models to assign it appropriately and update reward functions through predefined templates and adaptive weight by using weight decay and performance-based adjustments. Our approach enables the integration of nuanced human insights across various levels of quality, enhancing the interpretability and robustness of multi-agent cooperation. Empirical results in challenging environments demonstrate that M3HF significantly outperforms state-of-the-art methods, effectively addressing the complexities of reward design in MARL and enabling broader human participation in the training process.