CDI also offers a plausible approach for automatically making sense of competing arguments in a way that accords with the features enumerated here. This paper is part of an argument that it is now feasible to computationally instantiate a reasonable approximation of a coherence theory of truth [64]: the recent benchmark [12] provides additional quantitative evidence in this direction. By "hard-coding" acceptance of conclusively established propositions, this theory can furthermore be anchored in a correspondence theory of truth [65]. In other words, coherence computations can be required to incorporate privileged information that also coheres with observed reality. While it is easy to imagine attempts to try the same thing with privileged information that does not cohere with observed reality, lies cannot persist when they can easily be unraveled. Even with flawless technology (which this will not be), obstacles will be manifold. For example, in a pluralistic society, legal coherence may actually require sacrificing fairness in some ways [66]. Ultimately, people must decide matters for themselves. It is only reasonable to hope that technology can serve as a reliable tool to help people make their decisions more coherent.

Money laundering is a critical global issue for financial institutions. Automated Anti-money laundering (AML) models, like Graph Neural Networks (GNN), can be trained to identify illicit transactions in real time. A major issue for developing such models is the lack of access to training data due to privacy and confidentiality concerns. Synthetically generated data that mimics the statistical properties of real data but preserves privacy and confidentiality has been proposed as a solution. However, training AML models on purely synthetic datasets presents its own set of challenges. This article proposes the use of hybrid datasets to augment the utility of synthetic datasets by incorporating publicly available, easily accessible, and real-world features. These additions demonstrate that hybrid datasets not only preserve privacy but also improve model utility, offering a practical pathway for financial institutions to enhance AML systems.

Social norms govern culturally appropriate behavior in communication, enabling dialogue systems to produce responses that are not only coherent but also socially acceptable. We present NormGenesis, a multicultural framework for generating and annotating socially grounded dialogues across English, Chinese, and Korean. To model the dynamics of social interaction beyond static norm classification, we propose a novel dialogue type, Violation-to-Resolution (V2R), which models the progression of conversations following norm violations through recognition and socially appropriate repair. To improve pragmatic consistency in underrepresented languages, we implement an exemplar-based iterative refinement early in the dialogue synthesis process. This design introduces alignment with linguistic, emotional, and sociocultural expectations before full dialogue generation begins. Using this framework, we construct a dataset of 10,800 multi-turn dialogues annotated at the turn level for norm adherence, speaker intent, and emotional response. Human and LLM-based evaluations demonstrate that NormGenesis significantly outperforms existing datasets in refinement quality, dialogue naturalness, and generalization performance. We show that models trained on our V2R-augmented data exhibit improved pragmatic competence in ethically sensitive contexts. Our work establishes a new benchmark for culturally adaptive dialogue modeling and provides a scalable methodology for norm-aware generation across linguistically and culturally diverse languages.

Artificial intelligence risks are multidimensional in nature, as the same risk scenarios may have legal, operational, and financial risk dimensions. With the emergence of new AI regulations, the state of the art of artificial intelligence risk management seems to be highly immature due to upcoming AI regulations. Despite the appearance of several methodologies and generic criteria, it is rare to find guidelines with real implementation value, considering that the most important issue is customizing artificial intelligence risk metrics and risk models for specific AI risk scenarios. Furthermore, the financial departments, legal departments and Government Risk Compliance teams seem to remain unaware of many technical aspects of AI systems, in which data scientists and AI engineers emerge as the most appropriate implementers. It is crucial to decompose the problem of artificial intelligence risk in several dimensions: data protection, fairness, accuracy, robustness, and information security. Consequently, the main task is developing adequate metrics and risk models that manage to reduce uncertainty for decision-making in order to take informed decisions concerning the risk management of AI systems. The purpose of this paper is to orientate AI stakeholders about the depths of AI risk management. Although it is not extremely technical, it requires a basic knowledge of risk management, quantifying uncertainty, the FAIR model, machine learning, large language models and AI context engineering. The examples presented pretend to be very basic and understandable, providing simple ideas that can be developed regarding specific AI customized environments. There are many issues to solve in AI risk management, and this paper will present a holistic overview of the inter-dependencies of AI risks, and how to model them together, within risk scenarios.

Even though current generation of AI is underpinned by a common technology - namely machine learning, especially in the form of deep learning - in the public eye it has not emerged as a single solution. Rather, it has taken shape through multiple and overlapping applications - ranging from predictive diagnostics in healthcare and algorithmic hiring systems in HR to autonomous weapons and generative language models. As AI becomes increasingly embedded in sector - specific infrastructures, the question of how publics perceive its us e is gaining urgency. Existing literature on public perception of AI suggests that attitudes are highly sensitive to the application domain . People tend to be more supportive of AI in domains where it is perceived to augment human capacity (e.g., in medical diagnostics) and more sceptical when AI is seen as replacing judg e ment or threatening civil liberties or rights (e.g., in security or surveillance). These perceptions are shaped not only by technical features of the AI system but also by institutional trust, cultural attitude s toward risk, and the moral economy of the domain in question. Despite this, few reviews have systematically compared public perceptions across sectors and explored the cross - domain patterns and differences in attitudes.

Semantic parsing of long documents remains challenging due to quadratic growth in pairwise composition and memory requirements. We introduce \textbf{Hierarchical Segment-Graph Memory (HSGM)}, a novel framework that decomposes an input of length $N$ into $M$ meaningful segments, constructs \emph{Local Semantic Graphs} on each segment, and extracts compact \emph{summary nodes} to form a \emph{Global Graph Memory}. HSGM supports \emph{incremental updates} -- only newly arrived segments incur local graph construction and summary-node integration -- while \emph{Hierarchical Query Processing} locates relevant segments via top-$K$ retrieval over summary nodes and then performs fine-grained reasoning within their local graphs. Theoretically, HSGM reduces worst-case complexity from $O(N^2)$ to $O\!\left(N\,k + (N/k)^2\right)$, with segment size $k \ll N$, and we derive Frobenius-norm bounds on the approximation error introduced by node summarization and sparsification thresholds. Empirically, on three benchmarks -- long-document AMR parsing, segment-level semantic role labeling (OntoNotes), and legal event extraction -- HSGM achieves \emph{2--4$\times$ inference speedup}, \emph{$>60\%$ reduction} in peak memory, and \emph{$\ge 95\%$} of baseline accuracy. Our approach unlocks scalable, accurate semantic modeling for ultra-long texts, enabling real-time and resource-constrained NLP applications.

The advancement of Large Language Models (LLMs) has transformed natural language processing; however, their safety mechanisms remain under-explored in low-resource, multilingual settings. Here, we aim to bridge this gap. In particular, we introduce \textsf{SGToxicGuard}, a novel dataset and evaluation framework for benchmarking LLM safety in Singapore's diverse linguistic context, including Singlish, Chinese, Malay, and Tamil. SGToxicGuard adopts a red-teaming approach to systematically probe LLM vulnerabilities in three real-world scenarios: \textit{conversation}, \textit{question-answering}, and \textit{content composition}. We conduct extensive experiments with state-of-the-art multilingual LLMs, and the results uncover critical gaps in their safety guardrails. By offering actionable insights into cultural sensitivity and toxicity mitigation, we lay the foundation for safer and more inclusive AI systems in linguistically diverse environments.\footnote{Link to the dataset: https://github.com/Social-AI-Studio/SGToxicGuard.} \textcolor{red}{Disclaimer: This paper contains sensitive content that may be disturbing to some readers.}

Jimmy Kimmel's big TV comeback strangled as SEVENTY ABC affiliates refuse to air tonight's show Wall Street delivers clear verdict on Trump's Tylenol claims I was a devout Catholic... until I died. I'm the doctor on the cusp of an autism breakthrough... we're using an everyday $2.50 pill to reverse children's symptoms Secret Service foils'espionage' plot in NYC ahead of UN General Assembly that could have crashed Big Apple's phone network The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Sarah Ferguson sent Jeffrey Epstein fawning apology email'after he threatened to destroy her' in'Hannibal Lector-like' phone call The six hidden messages in the texts between Charlie Kirk's'assassin' and his trans lover DECODED Awkward moment Emmanuel Macron rings Trump for help after his motorcade is stopped by cops in New York... but ends up having to get out and walk Kate Middleton delivers a'mic drop' moment in dazzling gold dress identical to the late monarch - giving a glimpse of the Queen she plans to be William is urging his father to disown Fergie and Andrew over Epstein scandal... but King fears they could go rogue and values their loyalty Insiders speak out on Barack and Michelle Obama's secretive yacht vacation amid's**t show': 'They NEEDED this trip' MORE: Gabrielle surging into major hurricane as forecasters warn of'life-threatening' impact to East Coast Hurricane forecasters warn that the US East Coast could feel the wrath of two tropical cyclones at the same time next week. The National Hurricane Center (NHC) revealed their updated tracks for a pair of tropical disturbances brewing in the Atlantic, giving both a high likelihood of turning into named storms within seven days . One of these threatening weather systems, currently dubbed Invest 94L, is expected to form right off the coast of Florida . The other potential storm, called Invest 93L, has been developing in the middle of the Atlantic and forecasters unveiled an initial path that could take it towards the Carolinas by the end of the week.

Jimmy Kimmel's big TV comeback strangled as SEVENTY ABC affiliates refuse to air tonight's show Wall Street delivers clear verdict on Trump's Tylenol claims I was a devout Catholic... until I died. I'm the doctor on the cusp of an autism breakthrough... we're using an everyday $2.50 pill to reverse children's symptoms Secret Service foils'espionage' plot in NYC ahead of UN General Assembly that could have crashed Big Apple's phone network The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Sarah Ferguson sent Jeffrey Epstein fawning apology email'after he threatened to destroy her' in'Hannibal Lector-like' phone call The six hidden messages in the texts between Charlie Kirk's'assassin' and his trans lover DECODED Awkward moment Emmanuel Macron rings Trump for help after his motorcade is stopped by cops in New York... but ends up having to get out and walk Kate Middleton delivers a'mic drop' moment in dazzling gold dress identical to the late monarch - giving a glimpse of the Queen she plans to be William is urging his father to disown Fergie and Andrew over Epstein scandal... but King fears they could go rogue and values their loyalty Insiders speak out on Barack and Michelle Obama's secretive yacht vacation amid's**t show': 'They NEEDED this trip' Why Amazon Fresh failed: As all 19 remaining UK stores close, experts blame'dystopian' concept that stoked fears of Big Brother surveillance READ MORE: How artificial intelligence is ALREADY patrolling Britain's shops It was supposed to trigger a revolution in how we shop on the high street. But Amazon's'Fresh' shopping concept - which uses AI to track customers - may instead be remembered as a failed experiment in mass surveillance. The tech giant has announced that it has made the'difficult decision' to close all 19 Amazon Fresh stores in the UK - putting up to 250 workers at risk. Amazon claims the closures are the result of a'thorough evaluation of business operations and the very substantial growth opportunities in online delivery'.

Jimmy Kimmel's big TV comeback strangled as SEVENTY ABC affiliates refuse to air tonight's show Wall Street delivers clear verdict on Trump's Tylenol claims I was a devout Catholic... until I died. I'm the doctor on the cusp of an autism breakthrough... we're using an everyday $2.50 pill to reverse children's symptoms Secret Service foils'espionage' plot in NYC ahead of UN General Assembly that could have crashed Big Apple's phone network The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Sarah Ferguson sent Jeffrey Epstein fawning apology email'after he threatened to destroy her' in'Hannibal Lector-like' phone call The six hidden messages in the texts between Charlie Kirk's'assassin' and his trans lover DECODED Awkward moment Emmanuel Macron rings Trump for help after his motorcade is stopped by cops in New York... but ends up having to get out and walk Kate Middleton delivers a'mic drop' moment in dazzling gold dress identical to the late monarch - giving a glimpse of the Queen she plans to be William is urging his father to disown Fergie and Andrew over Epstein scandal... but King fears they could go rogue and values their loyalty Insiders speak out on Barack and Michelle Obama's secretive yacht vacation amid's**t show': 'They NEEDED this trip' Shake It Off! How Taylor Swift has ditched her Southern drawl in favour of a northern American accent, revealed In the 19 years since she released her first song, Taylor Swift's music been through several transitions, changing from country, to pop, to indie folk, and almost every genre in between. Now, a study has revealed how it's not just Swift's music that has evolved. Scientists from the University of Minnesota say the chart-topping singer's accent has also changed over time. In their study, the team analysed years of Swift's recorded interviews to track how her dialect has transformed.