Large Language Models Miss the Multi-Agent Markcs.AI updates on arXiv.org arXiv:2505.21298v4 Announce Type: replace-cross
Abstract: Recent interest in Multi-Agent Systems of Large Language Models (MAS LLMs) has led to an increase in frameworks leveraging multiple LLMs to tackle complex tasks. However, much of this literature appropriates the terminology of MAS without engaging with its foundational principles. In this position paper, we highlight critical discrepancies between MAS theory and current MAS LLMs implementations, focusing on four key areas: the social aspect of agency, environment design, coordination and communication protocols, and measuring emergent behaviours. Our position is that many MAS LLMs lack multi-agent characteristics such as autonomy, social interaction, and structured environments, and often rely on oversimplified, LLM-centric architectures. The field may slow down and lose traction by revisiting problems the MAS literature has already addressed. Therefore, we systematically analyse this issue and outline associated research opportunities; we advocate for better integrating established MAS concepts and more precise terminology to avoid mischaracterisation and missed opportunities.
arXiv:2505.21298v4 Announce Type: replace-cross
Abstract: Recent interest in Multi-Agent Systems of Large Language Models (MAS LLMs) has led to an increase in frameworks leveraging multiple LLMs to tackle complex tasks. However, much of this literature appropriates the terminology of MAS without engaging with its foundational principles. In this position paper, we highlight critical discrepancies between MAS theory and current MAS LLMs implementations, focusing on four key areas: the social aspect of agency, environment design, coordination and communication protocols, and measuring emergent behaviours. Our position is that many MAS LLMs lack multi-agent characteristics such as autonomy, social interaction, and structured environments, and often rely on oversimplified, LLM-centric architectures. The field may slow down and lose traction by revisiting problems the MAS literature has already addressed. Therefore, we systematically analyse this issue and outline associated research opportunities; we advocate for better integrating established MAS concepts and more precise terminology to avoid mischaracterisation and missed opportunities. Read More
How Sharp and Bias-Robust is a Model? Dual Evaluation Perspectives on Knowledge Graph Completioncs.AI updates on arXiv.org arXiv:2512.06296v1 Announce Type: new
Abstract: Knowledge graph completion (KGC) aims to predict missing facts from the observed KG. While a number of KGC models have been studied, the evaluation of KGC still remain underexplored. In this paper, we observe that existing metrics overlook two key perspectives for KGC evaluation: (A1) predictive sharpness — the degree of strictness in evaluating an individual prediction, and (A2) popularity-bias robustness — the ability to predict low-popularity entities. Toward reflecting both perspectives, we propose a novel evaluation framework (PROBE), which consists of a rank transformer (RT) estimating the score of each prediction based on a required level of predictive sharpness and a rank aggregator (RA) aggregating all the scores in a popularity-aware manner. Experiments on real-world KGs reveal that existing metrics tend to over- or under-estimate the accuracy of KGC models, whereas PROBE yields a comprehensive understanding of KGC models and reliable evaluation results.
arXiv:2512.06296v1 Announce Type: new
Abstract: Knowledge graph completion (KGC) aims to predict missing facts from the observed KG. While a number of KGC models have been studied, the evaluation of KGC still remain underexplored. In this paper, we observe that existing metrics overlook two key perspectives for KGC evaluation: (A1) predictive sharpness — the degree of strictness in evaluating an individual prediction, and (A2) popularity-bias robustness — the ability to predict low-popularity entities. Toward reflecting both perspectives, we propose a novel evaluation framework (PROBE), which consists of a rank transformer (RT) estimating the score of each prediction based on a required level of predictive sharpness and a rank aggregator (RA) aggregating all the scores in a popularity-aware manner. Experiments on real-world KGs reveal that existing metrics tend to over- or under-estimate the accuracy of KGC models, whereas PROBE yields a comprehensive understanding of KGC models and reliable evaluation results. Read More
(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License. Read More
AI Application in Anti-Money Laundering for Sustainable and Transparent Financial Systemscs.AI updates on arXiv.org arXiv:2512.06240v1 Announce Type: new
Abstract: Money laundering and financial fraud remain major threats to global financial stability, costing trillions annually and challenging regulatory oversight. This paper reviews how artificial intelligence (AI) applications can modernize Anti-Money Laundering (AML) workflows by improving detection accuracy, lowering false-positive rates, and reducing the operational burden of manual investigations, thereby supporting more sustainable development. It further highlights future research directions including federated learning for privacy-preserving collaboration, fairness-aware and interpretable AI, reinforcement learning for adaptive defenses, and human-in-the-loop visualization systems to ensure that next-generation AML architectures remain transparent, accountable, and robust. In the final part, the paper proposes an AI-driven KYC application that integrates graph-based retrieval-augmented generation (RAG Graph) with generative models to enhance efficiency, transparency, and decision support in KYC processes related to money-laundering detection. Experimental results show that the RAG-Graph architecture delivers high faithfulness and strong answer relevancy across diverse evaluation settings, thereby enhancing the efficiency and transparency of KYC CDD/EDD workflows and contributing to more sustainable, resource-optimized compliance practices.
arXiv:2512.06240v1 Announce Type: new
Abstract: Money laundering and financial fraud remain major threats to global financial stability, costing trillions annually and challenging regulatory oversight. This paper reviews how artificial intelligence (AI) applications can modernize Anti-Money Laundering (AML) workflows by improving detection accuracy, lowering false-positive rates, and reducing the operational burden of manual investigations, thereby supporting more sustainable development. It further highlights future research directions including federated learning for privacy-preserving collaboration, fairness-aware and interpretable AI, reinforcement learning for adaptive defenses, and human-in-the-loop visualization systems to ensure that next-generation AML architectures remain transparent, accountable, and robust. In the final part, the paper proposes an AI-driven KYC application that integrates graph-based retrieval-augmented generation (RAG Graph) with generative models to enhance efficiency, transparency, and decision support in KYC processes related to money-laundering detection. Experimental results show that the RAG-Graph architecture delivers high faithfulness and strong answer relevancy across diverse evaluation settings, thereby enhancing the efficiency and transparency of KYC CDD/EDD workflows and contributing to more sustainable, resource-optimized compliance practices. Read More
The holiday season compresses risk into a short, high-stakes window. Systems run hot, teams run lean, and attackers time automated campaigns to get maximum return. Multiple industry threat reports show that bot-driven fraud, credential stuffing and account takeover attempts intensify around peak shopping events, especially the weeks around Black Friday and Christmas. Why holiday peaks Read […]
The Iranian hacking group known as MuddyWater has been observed leveraging a new backdoor dubbed UDPGangster that uses the User Datagram Protocol (UDP) for command-and-control (C2) purposes. The cyber espionage activity targeted users in Turkey, Israel, and Azerbaijan, according to a report from Fortinet FortiGuard Labs. “This malware enables remote control of compromised systems by […]
It’s been a week of chaos in code and calm in headlines. A bug that broke the internet’s favorite framework, hackers chasing AI tools, fake apps stealing cash, and record-breaking cyberattacks — all within days. If you blink, you’ll miss how fast the threat map is changing. New flaws are being found, published, and exploited […]
Attacks against CVE-2025-55182, which began almost immediately after public disclosure last week, have increased as more threat actors take advantage of the flaw. Read More
The AI Bubble Will Pop — And Why That Doesn’t MatterTowards Data Science How history’s biggest tech bubble explains where AI is headed next
The post The AI Bubble Will Pop — And Why That Doesn’t Matter appeared first on Towards Data Science.
How history’s biggest tech bubble explains where AI is headed next
The post The AI Bubble Will Pop — And Why That Doesn’t Matter appeared first on Towards Data Science. Read More
OpenAI: Enterprise users swap AI pilots for deep integrationsAI News According to OpenAI, enterprise AI has graduated from the sandbox and is now being used for daily operations with deep workflow integrations. New data from the company shows that firms are now assigning complex and multi-step workflows to models rather than simply asking for text summaries. The figures illustrate a hard change in how organisations
The post OpenAI: Enterprise users swap AI pilots for deep integrations appeared first on AI News.
According to OpenAI, enterprise AI has graduated from the sandbox and is now being used for daily operations with deep workflow integrations. New data from the company shows that firms are now assigning complex and multi-step workflows to models rather than simply asking for text summaries. The figures illustrate a hard change in how organisations
The post OpenAI: Enterprise users swap AI pilots for deep integrations appeared first on AI News. Read More