Knowledge editing aims to efficiently update Large Language Models (LLMs) by modifying specific knowledge without retraining the entire model. Among knowledge editing approaches, in-context editing (ICE) offers a lightweight solution by injecting new knowledge directly into the input context, leaving model parameters unchanged. However, existing ICE approaches do not explicitly separate the newly injected knowledge from the model's original reasoning process. This entanglement often results in conflicts between external updates and internal parametric knowledge, undermining the consistency and accuracy of the reasoning path.In this work, we conduct preliminary experiments to examine how parametric knowledge influences reasoning path planning. We find that the model's reasoning is tightly coupled with its internal knowledge, and that naively injecting new information without adapting the reasoning path often leads to performance degradation, particularly in multi-hop tasks. To this end, we propose DecKER, a novel ICE framework that decouples reasoning from knowledge editing by generating a masked reasoning path and then resolving knowledge edits via hybrid retrieval and model-based validation. Experiments on multi-hop QA benchmarks show that DecKER significantly outperforms existing ICE methods by mitigating knowledge conflicts and preserving reasoning consistency. Our code is available at: https://github.com/bebr2/DecKER .

As large language models (LLMs) continue to advance, the need for up-to-date and well-organized benchmarks becomes increasingly critical. However, many existing datasets are scattered, difficult to manage, and make it challenging to perform evaluations tailored to specific needs or domains, despite the growing importance of domain-specific models in areas such as math or code. In this paper, we introduce BenchHub, a dynamic benchmark repository that empowers researchers and developers to evaluate LLMs more effectively. BenchHub aggregates and automatically classifies benchmark datasets from diverse domains, integrating 303K questions across 38 benchmarks. It is designed to support continuous updates and scalable data management, enabling flexible and customizable evaluation tailored to various domains or use cases. Through extensive experiments with various LLM families, we demonstrate that model performance varies significantly across domain-specific subsets, emphasizing the importance of domain-aware benchmarking. We believe BenchHub can encourage better dataset reuse, more transparent model comparisons, and easier identification of underrepresented areas in existing benchmarks, offering a critical infrastructure for advancing LLM evaluation research.

As large language models (LLMs) become more powerful and pervasive across society, ensuring these systems are beneficial, safe, and aligned with human values is crucial. Current alignment techniques, like Constitutional AI (CAI), involve complex iterative processes. This paper argues that the Method of Wide Reflective Equilibrium (MWRE) -- a well-established coherentist moral methodology -- offers a uniquely apt framework for understanding current LLM alignment efforts. Moreover, this methodology can substantively augment these processes by providing concrete pathways for improving their dynamic revisability, procedural legitimacy, and overall ethical grounding. Together, these enhancements can help produce more robust and ethically defensible outcomes. MWRE, emphasizing the achievement of coherence between our considered moral judgments, guiding moral principles, and relevant background theories, arguably better represents the intricate reality of LLM alignment and offers a more robust path to justification than prevailing foundationalist models or simplistic input-output evaluations. While current methods like CAI bear a structural resemblance to MWRE, they often lack its crucial emphasis on dynamic, bi-directional revision of principles and the procedural legitimacy derived from such a process. While acknowledging various disanalogies (e.g., consciousness, genuine understanding in LLMs), the paper demonstrates that MWRE serves as a valuable heuristic for critically analyzing current alignment efforts and for guiding the future development of more ethically sound and justifiably aligned AI systems.

Many large enterprises that operate highly governed and complex ICT environments have no efficient and effective way to support their Data and AI teams in rapidly spinning up and tearing down self-service data and compute infrastructure, to experiment with new data analytic tools, and deploy data products into operational use. This paper proposes a key piece of the solution to the overall problem, in the form of an on-demand self-service data-platform infrastructure to empower de-centralised data teams to build data products on top of centralised templates, policies and governance. The core innovation is an efficient method to leverage immutable container operating systems and infrastructure-as-code methodologies for creating, from scratch, vendor-neutral and short-lived Kubernetes clusters on-premises and in any cloud environment. Our proposed approach can serve as a repeatable, portable and cost-efficient alternative or complement to commercial Platform-as-a-Service (PaaS) offerings, and this is particularly important in supporting interoperability in complex data mesh environments with a mix of modern and legacy compute infrastructure.

Retrieval-Augmented Generation (RAG) systems, which integrate Large Language Models (LLMs) with external knowledge sources, are vulnerable to a range of adversarial attack vectors. This paper examines the importance of RAG systems through recent industry adoption trends and identifies the prominent attack vectors for RAG: prompt injection, data poisoning, and adversarial query manipulation. We analyze these threats under risk management lens, and propose robust prioritized control list that includes risk-mitigating actions like input validation, adversarial training, and real-time monitoring.

Contextual large language model embeddings are increasingly utilized for topic modeling and clustering. However, current methods often scale poorly, rely on opaque similarity metrics, and struggle in multilingual settings. In this work, we present a novel, scalable, interpretable, hierarchical, and multilingual approach to clustering news articles and social media data. To do this, we first train multilingual Matryoshka embeddings that can determine story similarity at varying levels of granularity based on which subset of the dimensions of the embeddings is examined. This embedding model achieves state-of-the-art performance on the SemEval 2022 Task 8 test dataset (Pearson $ρ$ = 0.816). Once trained, we develop an efficient hierarchical clustering algorithm that leverages the hierarchical nature of Matryoshka embeddings to identify unique news stories, narratives, and themes. We conclude by illustrating how our approach can identify and cluster stories, narratives, and overarching themes within real-world news datasets.

With the growing importance of AI governance, numerous high - level frameworks and principles have been articulated by policymakers, institutions, and expert communities to guide the development and application of AI . While such frameworks offer valuable normative orientation, they may not fully capture the practical concerns of those who interact with AI systems in organizational and operational contexts. To address this gap, this study adopts a bottom - up approach to explore how governance - relevant themes are expressed in user discourse. Drawing on over 100,000 user reviews of AI products from G2.com, we apply BERTopic to extract latent themes and identify those most semantically related to AI governance. The analysis reveals a diverse set of governance - relevant topics spanning both technical and non - technical domains. These include concerns across organizational processes -- such as planning, coordination, and communication -- as well as stages of the AI value chain, includ ing deployment infrastructure, data handling, and analytics. The findings show considerable overlap with institutional AI governance and ethics frameworks on issues like privacy and transparency, but also surface overlooked areas such as project management, strategy development, and customer interaction. This highlights the need for more empirically grounded, user - centered approaches to AI governance -- approaches that complement normative models by capturing how governance unfolds in applied settings . By foregrounding how governance is enacted in practice, this study contributes to more inclusive and operationally grounded approaches to AI governance and digital policy.

The rapid integration of Large Language Models (LLMs) in high-stakes decision-making -- such as allocating scarce resources like donor organs -- raises critical questions about their alignment with human moral values. We systematically evaluate the behavior of several prominent LLMs against human preferences in kidney allocation scenarios and show that LLMs: i) exhibit stark deviations from human values in prioritizing various attributes, and ii) in contrast to humans, LLMs rarely express indecision, opting for deterministic decisions even when alternative indecision mechanisms (e.g., coin flipping) are provided. Nonetheless, we show that low-rank supervised fine-tuning with few samples is often effective in improving both decision consistency and calibrating indecision modeling. These findings illustrate the necessity of explicit alignment strategies for LLMs in moral/ethical domains.

In the age of social media, understanding public sentiment toward major corporations is crucial for investors, policymakers, and researchers. This paper presents a comprehensive sentiment analysis system tailored for corporate reputation monitoring, combining Natural Language Processing (NLP) and machine learning techniques to accurately interpret public opinion in real time. The methodology integrates a hybrid sentiment detection framework leveraging both rule-based models (VADER) and transformer-based deep learning models (DistilBERT), applied to social media data from multiple platforms. The system begins with robust preprocessing involving noise removal and text normalization, followed by sentiment classification using an ensemble approach to ensure both interpretability and contextual accuracy. Results are visualized through sentiment distribution plots, comparative analyses, and temporal sentiment trends for enhanced interpretability. Our analysis reveals significant disparities in public sentiment across major corporations, with companies like Amazon (81.2) and Samsung (45.8) receiving excellent sentiment scores, while Microsoft (21.7) and Walmart (21.9) exhibit poor sentiment profiles. These findings demonstrate the utility of our multi-source sentiment framework in providing actionable insights regarding corporate public perception, enabling stakeholders to make informed strategic decisions based on comprehensive sentiment analysis.

Ukraine's surprise Sunday attack on Russian offensive weapons caches may be a good time for the U.S. to reflect on its own weaknesses, should one of its adversaries attempt a similar strike. Col. Seth Krummrich, a retired Army Special Forces commander and vice president at the Virginia-based security firm Global Guardian, warned that the U.S. remains vulnerable to drone attacks. "Interestingly, it is not a technological gap, it is a policy/authority process to engage and deny drone attacks," Krummrich said. "I assess it will take a major drone strike in the U.S. to change policy." Even civilian operations have a tough time getting approval for drone-interception-authority protections, the NFL excepted, he said.