Bio-inspired algorithms utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. However, a plethora of these algorithms require a more rigorous review before making them applicable to the relevant fields. This survey categorizes these algorithms into eight groups: evolutionary, swarm intelligence, physics-inspired, ecosystem and plant-based, predator-prey, neural-inspired, human-inspired, and hybrid approaches, and reviews their principles, strengths, novelty, and critical limitations. We provide a critique on the novelty issues of many of these algorithms. We illustrate some of the suitable usage of the prominent algorithms in machine learning, engineering design, bioinformatics, and intelligent systems, and highlight recent advances in hybridization, parameter tuning, and adaptive strategies. Finally, we identify open challenges such as scalability, convergence, reliability, and interpretability to suggest directions for future research. This work aims to serve as a resource for both researchers and practitioners interested in understanding the current landscape and future directions of reliable and authentic advancement of bio-inspired algorithms.

Shapes of cognition is a new conceptual paradigm for the computational cognitive modeling of Language - Endowed Intelligent Agents (LEIAs) . S hapes are remembered constellations of sensory, linguistic, conceptual, episodic, and procedural knowledge that allow agents to cut through the complexity of real life the same way as people do: by expecting things to be typical, recognizing patterns, acting by habit, reasoning by analogy, satisficing, and generally minimizing cognitive load to the degree situations permit . Atypical outcomes are treated using shapes - based recovery method s, such as learning on the fly, asking a human partner for help, or seeking an actionable, even if imperfect, situational understanding . Although shapes is an umbrella term, it is not vague: shapes - based modeling involves particular objectives, hypotheses, modeling strategies, knowledge bases, and actual models of wide - ranging phenomena, all implemented within a particular cognitive architecture . Such s pecificity is needed both to vet the our hypotheses and to achieve our practical aims of building useful agent systems that are explainable, extensible, and worthy of our trust, even in critical domains . However, a lthough the LEIA example of shapes - based modeling is specific, the principles can be applied more broadly, giving new life to knowledge - based and hybrid AI .

Our framework, HARMONIC (Human-AI Robotic Team Member Operating with Natural Intelligence and Communication, Figure 1), is an implemented dual-control cognitive robotic architecture featuring distinct layers of strategic reasoning and tactical, skill-level control [20]. This approach advances the hybrid control systems and architectures reviewed by Dennis et al. [21] and contrasts with DIARC's [22], [23] integration strategy, which embeds the strategic layer within the tactical layer to support concurrent operation. The strategic layer of HARMONIC adapts a mature cognitive architecture, OntoAgent [24], [25], [17] for high-level reasoning, leveraging explicit, structured knowledge representations that can be inspected, verified, and incre-mentally expanded.

As artificial intelligence advances toward artificial general intelligence (AGI), the need for robust and human-like memory systems has become increasingly evident. Current memory architectures often suffer from limited adaptability, insufficient multimodal integration, and an inability to support continuous learning. To address these limitations, we propose a scenario-driven methodology that extracts essential functional requirements from representative cognitive scenarios, leading to a unified set of design principles for next-generation AI memory systems. Based on this approach, we introduce the \textbf{COgnitive Layered Memory Architecture (COLMA)}, a novel framework that integrates cognitive scenarios, memory processes, and storage mechanisms into a cohesive design. COLMA provides a structured foundation for developing AI systems capable of lifelong learning and human-like reasoning, thereby contributing to the pragmatic development of AGI.

The cost and complexity of financial crime compliance (FCC) continue to rise, often without measurable improvements in effectiveness. While AI offers potential, most solutions remain opaque and poorly aligned with regulatory expectations. This paper presents the design and deployment of an agentic AI system for FCC in digitally native financial platforms. Developed through an Action Design Research (ADR) process with a fintech firm and regulatory stakeholders, the system automates onboarding, monitoring, investigation, and reporting, emphasizing explainability, traceability, and compliance-by-design. Using artifact-centric modeling, it assigns clearly bounded roles to autonomous agents and enables task-specific model routing and audit logging. The contribution includes a reference architecture, a real-world prototype, and insights into how Agentic AI can reconfigure FCC workflows under regulatory constraints. Our findings extend IS literature on AI-enabled compliance by demonstrating how automation, when embedded within accountable governance structures, can support transparency and institutional trust in high-stakes, regulated environments.

Persistent monitoring using robot teams is of interest in fields such as security, environmental monitoring, and disaster recovery. Performing such monitoring in a fully on-line decentralised fashion has significant potential advantages for robustness, adaptability, and scalability of monitoring solutions, including, in principle, the capacity to effectively adapt in real-time to a changing environment. We examine this through the lens of multi-robot patrol, in which teams of patrol robots must persistently minimise time between visits to points of interest, within environments where traversability of routes is highly dynamic. These dynamics must be observed by patrol agents and accounted for in a fully decentralised on-line manner. In this work, we present a new method of monitoring and adjusting for environment dynamics in a decentralised multi-robot patrol team. We demonstrate that our method significantly outperforms realistic baselines in highly dynamic scenarios, and also investigate dynamic scenarios in which explicitly accounting for environment dynamics may be unnecessary or impractical.

This work introduces xOffense, an AI-driven, multi-agent penetration testing framework that shifts the process from labor-intensive, expert-driven manual efforts to fully automated, machine-executable workflows capable of scaling seamlessly with computational infrastructure. At its core, xOffense leverages a fine-tuned, mid-scale open-source LLM (Qwen3-32B) to drive reasoning and decision-making in penetration testing. The framework assigns specialized agents to reconnaissance, vulnerability scanning, and exploitation, with an orchestration layer ensuring seamless coordination across phases. Fine-tuning on Chain-of-Thought penetration testing data further enables the model to generate precise tool commands and perform consistent multi-step reasoning. We evaluate xOffense on two rigorous benchmarks: AutoPenBench and AI-Pentest-Benchmark. The results demonstrate that xOffense consistently outperforms contemporary methods, achieving a sub-task completion rate of 79.17%, decisively surpassing leading systems such as VulnBot and PentestGPT. These findings highlight the potential of domain-adapted mid-scale LLMs, when embedded within structured multi-agent orchestration, to deliver superior, cost-efficient, and reproducible solutions for autonomous penetration testing.

Large Language Models (LLMs) have revolutionized the simulation of agent societies, enabling autonomous planning, memory formation, and social interactions. However, existing frameworks often overlook systematic evaluations for event organization and lack visualized integration with physically grounded environments, limiting agents' ability to navigate spaces and interact with items realistically. We develop Mini-AgentPro, a visualization platform featuring an intuitive map editor for customizing environments and a simulation player with smooth animations. Based on this tool, we introduce a comprehensive test set comprising eight diverse event scenarios with basic and hard variants to assess agents' ability. Evaluations using GPT -4o demonstrate strong performance in basic settings but highlight coordination challenges in hard variants.

Benchmarks are crucial for assessing multi-agent reinforcement learning (MARL) algorithms. While StarCraft II-related environments have driven significant advances in MARL, existing benchmarks like SMAC focus primarily on mi-cromanagement, limiting comprehensive evaluation of high-level strategic intelligence. To address this, we introduce HLSMAC, a new cooperative MARL benchmark with 12 carefully designed StarCraft II scenarios based on classical stratagems from the Thirty-Six Stratagems. Each scenario corresponds to a specific stratagem and is designed to challenge agents with diverse strategic elements, including tactical maneuvering, timing coordination, and deception, thereby opening up avenues for evaluating high-level strategic decision-making capabilities. We also propose novel metrics across multiple dimensions beyond conventional win rate, such as ability utilization and advancement efficiency, to assess agents' overall performance within the HLSMAC environment. We integrate state-of-the-art MARL algorithms and LLM-based agents with our benchmark and conduct comprehensive experiments. The results demonstrate that HLSMAC serves as a robust testbed for advancing multi-agent strategic decision-making.

Abstract-- Establishing standardized metrics for Social Robot Navigation (SRN) algorithms for assessing the quality and social compliance of robot behavior around humans is essential for SRN research. Currently, commonly used evaluation metrics lack the ability to quantify how cooperative an agent behaves in interaction with humans. Concretely, in a simple frontal approach scenario, no metric specifically captures if both agents cooperate or if one agent stays on collision course and the other agent is forced to evade. T o address this limitation, we propose two new metrics, a conflict intensity metric and the responsibility metric. T ogether, these metrics are capable of evaluating the quality of human-robot interactions by showing how much a given algorithm has contributed to reducing a conflict and which agent actually took responsibility of the resolution. This work aims to contribute to the development of a comprehensive and standardized evaluation methodology for SRN, ultimately enhancing the safety, efficiency, and social acceptance of robots in human-centric environments.