AI ethics on Thursday, 2 July 2026

This content is based on victim and potential victim accounts. Government agencies and legitimate business names and phone numbers are often used by scam artists to take advantage of people. Description The scam showed Anderson Cooper tal ... (https://incidentdatabase.ai/cite/1564#7482)

A social media scam is using deepfakes of celebrities to promote products they falsely claim could cure Alzheimer's disease and dementia, the Better Business Bureau warned. Some victims they even found reoccurring charges after buying the h ... (https://incidentdatabase.ai/cite/1564#7483)

Scientists are using AI and physics-based simulations together to design new peptides that will kill previously drug-resistant bacteria.

FTC urged to reject Elon Musk’s bid to end X monitoring amid AI concerns.

Google tries balancing AI data center emissions with clean energy efforts.

Improved institutional safeguards and stricter regulations have pushed the burdens of protection and risk reduction on to Australian businesses.

Expect more compressed patching cycles from Apple going forward, as attackers leverage artificial intelligence to reduce time to exploit.

The rules for plants can be different in water.

The medical tools of the Revolutionary period help flesh out the picture of what physical well-being felt like for people living in the American colonies 250 years ago.

Companies are being too conservative in how they interpret FDA's AI guidance, but the agency can do more to help, too, Tala Fakhouri says.

The share of young people using AI chatbots for mental health advice rose more than 40% in a single year, researcher writes.

Tencent today launched Just Dance: Party, a mobile rhythm game based on Ubisoft’s Just Dance franchise, across iOS, Android and HarmonyOS devices. The game uses a proprietary AI-powered skeletal tracking algorithm that enables full-body motion capture through a smartphone’s front-facing camera, eliminating the need for external accessories such as dance mats or motion controllers. Players […]

During a recent conference on the People’s Liberation Army, I heard the same question posed to attendees and paper writers: “How would China react to U.S. force posture change X, Y, or Z?” or “How would the Chinese military respond to U.S. strikes in certain locations?” Having participated in dozens of unclassified wargames at the RAND Corporation and elsewhere, I hear a similar refrain when playing the “red team.”This is a reasonable and legitimate question. Policymakers and war planners should

Last month, mathematics researchers released the Leiden Declaration, a treatise on how to handle the challenges that artificial intelligence poses to their field. C. Brandon Ogbunu argues that biologists should learn from the mathematics community and work on their own version.

Drawn by affordable international schooling rather than glitz, Chinese students in the UAE are quietly rethinking how they see education, work, and future opportunities.

The province has set out plans to assess the current state of the industry surrounding the naturally occurring but addictive and cancer-causing stimulant before proceeding with legislation.

CSET’s Jessica Ji shared her expert insight in an article published by CNBC. The article examines the House passage of the Kids Internet and Digital Safety (KIDS) Act, a bill aimed at strengthening protections for minors online through age verification, content restrictions, and parental oversight tools. The post A major online safety bill for kids just passed the House. Here’s what experts say parents need to know appeared first on Center for Security and Emerging Technology .

We recently ran a strategy fellowship through Astra. As part of this, we ran a reading group for our fellows on some of the topics that we think are important for thinking about AI futurism (key dynamics in AI development, existential risk from AI, and approaches to mitigating risk). This post contains the reading list we used.

New Jersey Governor Mikie Sherrill has signed A5328, banning the sale of sensitive data and creating a data broker registry in New Jersey. The ban on sale applies to all forms of sensitive data under the New Jersey Data Privacy Act and applies to all entities regardless of the number of consumers whose data the … Continued

X Corp. should not be able to escape privacy compliance because it changed its name. On May 15, X Corp. filed a petition before the Federal Trade Commission (FTC) to set aside or modify an order issued in 2022 requiring the company to report regularly to the FTC for its violations of user data. The order or “consent decree” is a result of misleading the platforms’ 140 million users by using private information given to secure accounts, like phone numbers and email addresses, for targeted adverti

Adobe is experimenting with “agentic sites” that generate pages around an individual user’s intent. At AIEWF, we talked to Carlos Sanchez about the Web's future.

Scott’s foreword: Cynthia Dwork is Gordon McKay Professor of Computer Science at Harvard, and a pioneer in the fields of differential privacy and algorithmic fairness. On my recent travels to the SigmaWest science camp and then STOC, there was much talk about a recent Trump administration action that would ban not only differential privacy, but […]

The Federal Bureau of Investigation (FBI) said today it worked with industry partners to seize hundreds of domains associated with NetNut, a sprawling residential proxy service operated by the publicly-traded Israeli company Alarum Technologies [NASDAQ: ALAR]. The action comes roughly two weeks after KrebsOnSecurity published findings from multiple security firms connecting NetNut to the Popa botnet, a collection of at least two million devices that have been compromised by malicious software wi

Antitrust agencies have a habit of giving new labels to old anxieties. In artificial intelligence, the latest worry is that partnerships between large technology firms and startups are not partnerships at all, but mergers in clever disguises. In the first article in this series, we examined how Brazil’s Administrative Council for Economic Defense (CADE) has ... Killer Theories and Acqui-Hire Alibis The post Killer Theories and Acqui-Hire Alibis appeared first on Truth on the Market .

Paul Bakaus talks to us about Impeccable, human judgment in a 'loopmaxxing' era, and why agents still need people to steer them.

For roughly 90 years, Humphrey’s Executor had been the constitutional law equivalent of a load-bearing antique: an awkward, if still functioning, architectural kludge, much admired in certain circles, but increasingly hard to rationalize. Earlier this week, finally, the U.S. Supreme Court replaced it. In Trump v. Slaughter, the Court overruled that 1935 opinion. The president ... Much Ado About Removal: The Supreme Court, the FTC, and the End of Independent-ish Agencies The post Much Ado About R

Columbia Global Freedom of Expression seeks to contribute to the development of an integrated and progressive jurisprudence and understanding on freedom of expression and information around the world. It maintains an extensive database of international case law. This is its newsletter dealing with recent developments in the field. Across Kenya, more than 350 anti-government protesters were arrested last […]

OpenAI’s proposal to hand the U.S. government a 5 percent equity stake has made a theoretical debate over the relationship between private corporations and the government into an immediately relevant ...

Watch now | Watch my 64-minute course with lessons from the world's first agentic AI governance framework

On Tuesday, the United States Department of Commerce removed restrictions on two of Anthropic’s new advanced AI models that have prompted security concerns: Mythos 5 and Fable 5. This is a major ...

Published on June 26, 2026 9:54 PM GMT   Bullet point summary: Cultivated meat could have a price between $15/kg and $30/kg according to reputable technoeconomic analysis, which we review and explain. We present an interactive demand-side economic model where we translate that price to market share: https://pabloamc.github.io/Cultivated_meat/interactive.html Some species, like pork and especially chicken, are tough to replace with cultivated meat. Others, like cows and seafood, are more sig

Passed and signed with little warning, New Jersey's new law mandates unprecedented registration fees for data brokers and data collectors.

This final rule removes duplicative and unnecessary sections from the Runaway and Homeless Youth Program regulations. These amendments will streamline the Runaway and Homeless Youth Program regulations to make them more accessible to the public.

The Securities and Exchange Commission (the "Commission" or the "SEC") requests public comment on exchange-traded funds ("ETFs") seeking to invest in innovative asset classes or engage in novel investment strategies. We seek comment on ways to facilitate innovation in the ETF space while protecting investors, maintaining fair, orderly, and efficient markets, and facilitating capital formation.

On Tuesday, the Communications and Digital Committee continued looking into AI and copyright, speaking to Liz Kendall MP, Secretary of State for Science, Innovation and Technology and Lisa Nandy MP, ...

This letter provides GAO's comments on the American Institute of Certified Public Accountants (AICPA) Auditing Standards Board's (ASB) Proposed Statement on Standards for Attestation Engagements: Common Concepts, Examination Engagements, Review Engagements, and Engagements to Report on Sustainability Information and Proposed Statement on Standards for Attestation Engagements: Amendments to SSAE Nos. 18-19 and 21 to Reflect Proposed SSAE Common Concepts, Examination Engagements, Review Engagement

For the world’s poorest people, climate change does not announce itself in parts per million. It arrives as a ruined harvest, a flooded shopfront, and lost learning as children are kept out of school.

What GAO Found In May 2025, GAO identified five priority recommendations for the Board of Governors of the Federal Reserve System. Since then, the Federal Reserve has not implemented any of these recommendations. GAO is highlighting the following three areas that warrant timely and focused attention: Strengthening bank supervision, Analyzing regulations, and Addressing blockchain technology risks. Addressing GAO's recommendations in these areas would help the Federal Reserve reduce the risk of i

What GAO Found In May 2025, GAO identified nine priority recommendations for the Nuclear Regulatory Commission (NRC). Since then, NRC has not implemented any of these recommendations. In May 2026, GAO identified two additional priority recommendations, bringing the total to 11. GAO is highlighting the following three areas that warrant timely and focused attention: Addressing the security of radiological sources, Improving risk-informed decision-making, and Licensing advanced nuclear reactors. A

What GAO Found Under federal special education law, students with disabilities are to be educated alongside their peers without disabilities to the maximum extent appropriate. Nationally, the number of students with disabilities in the general education classroom (gen ed) for at least 40 percent of their day increased 25 percent from school year 2012–13 through school year 2023–24 (see figure). The largest increase came from students with disabilities in gen ed for at least 80 percent of their d

What GAO Found GAO has previously found that the Department of Energy’s (DOE) Office of Environmental Management (EM) has not followed its standards for defining mission need for some large projects. A mission need statement documents DOE’s identification of a mission-related need and, according to DOE standards, should not identify a particular solution. This ensures that DOE does not limit potential solutions at the project initiation stage. However, the majority of mission need statements tha

What GAO Found The Department of Defense (DOD) continues to struggle to deliver technologies quickly and within budget. Since its last annual assessment, GAO found: Programs are delaying interim events and milestones for some of the costliest major defense acquisition programs (MDAP). DOD has increased its use of the middle tier of acquisition (MTA) rapid prototyping and fielding pathways—intended to be completed in 5 years. Some programs began on the MTA pathway with technologies that require m

What GAO Found The Department of Homeland Security’s (DHS) Federal Emergency Management Agency (FEMA), the Department of Housing and Urban Development (HUD), and the Department of Energy (DOE) have obligated about $14 billion for Puerto Rico’s grid recovery and modernization since 2017, but limited funding has been disbursed. About $2.7 billion of about $11.1 billion obligated by FEMA has been disbursed since 2017, largely for equipment and materials and architecture and engineering. In addition

New EU AI guidance sets practical expectations for labelling, deepfakes and AI-generated content transparency. In Brief Companies are increasingly using AI to create or modify content across marketing, communications and customer-facing channels. As EU transparency obligations under the AI Act move closer to application, this raises practical and operational questions around when AI-generated or AI-manipulated [...] The post New EU guidance on AI transparency: what should companies be doing from

The Country Partnership Framework (CPF) for Benin FY27–36 outlines the World Bank Group’s strategy to support economic transformation, resilience, and large-scale job creation aligned with Benin’s ...

Inference-time alignment steers a frozen language model during decoding using auxiliary reward signals, avoiding the cost of repeated weight updates. However, existing inference-time alignment methods typically optimize a single scalar score, so explicit safety constraints must either be ignored or encoded through manually tuned penalties. We propose Lagrangian Reward Augmentation (LARA), a general inference-time alignment framework under safety constraints. Starting from a KL-regularized constr

Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data. However, no existing framework fully unifies automated transformation, readiness assessment, provenance tracking, and agent-native deployment. We present REDI, an open-source framework that addresses this gap through a unified five-stage pipeline (ingest, preprocess, transform, structure, and output) with per-stage instrumentation for repro

There is no doubt that safety alignment is an essential step in LLM training. However, conceptually it does not distinguish between various domains and the level of potential harm of a query, which creates significant complications in the fields like cyber security, where a model should not be constrained by its safety circuits to accomplish the goals of legitimate, authorized operations. In this work, we share our findings from a large scale abliteration experiment on 24 open-source LLMs and sh

In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents. Layered on top, an open-source RSE-Plugins ecosystem encodes accumulated RSE knowledge as a Plugin-Agent-Skill hierarchy spanning scientific Python practice, domain-specific knowledge, a six-phase resea

Local pairwise comparisons are a standard tool for learning how people want decision rules to work, e.g., in participatory design or alignment. However, their use builds in two strong assumptions: that local comparisons are sufficient evidence about how a person wants an automated decision rule to behave, and that people can always answer those comparisons decisively. We investigate how these assumptions may be compromised under internal pluralism: the idea that an individual evaluates decision

Despite alignment training, LLMs remain prone to generating unsafe outputs at deployment time. Monitoring outputs online and raising an alarm when safety can no longer be assumed is therefore critical. We study a simple real-time monitor that turns a verifier signal from an external model into an alarm decision by thresholding, with the threshold calibrated via risk control. In experiments on mathematical reasoning and red teaming datasets, we show that this simple design is competitive with mor

LLM agents will increasingly act in socially structured settings where role, audience, and relational context can shape what is advantageous or costly to say. We study whether such social structure, without any explicit objective in the prompt, changes what an agent expresses publicly relative to an off-the-record (OTR) channel elicited under the same condition. We introduce a dual-channel debate framework in which agents produce public utterances that enter the shared history alongside OTR resp

Vision-Language-Action (VLA) models are fundamentally bottlenecked by the scarcity of expert demonstrations -- triplets of observations, instructions, and actions that are costly to collect at scale. We argue that this bottleneck stems from conflating two distinct learning objectives: acquiring physical competence (how to move) and acquiring semantic alignment (what to do). Crucially, only the latter requires language supervision. Building on this Decomposition Hypothesis, we propose Task-Agnost

Compiler missed optimizations refer to cases in which compilers failed to optimize certain code. It takes many compiler developers' efforts to implement or patch such missed optimizations. In this paper, we present a systematic study of how well agents patch compiler missed optimizations. We identify a significant challenge that patching a missed optimization requires more than just fixing the reported case, and instead requires generalizing to similar cases. We construct a benchmark of real-wor

Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models. Existing approaches to exploiting this potential typically involve 1) training or fine-tuning generators, or 2) using lightweight post-hoc adaptation like prompt engineering or inference-time guidance, making them generator-specific and expertise-intensive. We study a complementary question: given a fixed pool of generated images, can downstream utility be improved

Mental health disorders affect nearly one billion people globally, yet 75% of individuals in low- and middle-income countries receive no treatment due to workforce shortages, cost barriers, and stigma. Current AI-powered wellness solutions predominantly rely on single-mode conversational interfaces that suffer high abandonment rates and fail to provide measurable, immediate relief calibrated to users' dynamic emotional states. This paper presents Copewell, a novel multi-agent swarm system design

The complexity of waste disposal regulations across European countries poses significant challenges for the residents and hinders the transition to a Circular Economy. In Germany, the proper sorting and disposal of household waste remains challenging across municipalities. Consequently, substantially reducing incorrectly disposed waste is vital for improving waste management and advancing the Circular Economy. AI-based waste sorting solutions can support residents through user-friendly tools, su

Vision-Language Navigation has increasingly emphasized high-level instruction reasoning, memory, global map construction, and instruction decomposition, while the low-level action representation remains comparatively underexplored. We propose CoFL-S, a low-level vision-language-action framework that predicts a language-conditioned flow field over the robot's local visible sector and generates continuous trajectories by rolling out the predicted field. To train this low-level representation, we c

The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this imperative, this paper presents an overview of AI risk assessment (identification and analysis) and management methodologies. It begins by reviewing the worldwide regulatory landscape that drives the need for systematic AI risk assessment. Then we characterize the spectrum of AI-related risks identified in the literature, fr

Deep learning has achieved remarkable performance in medical image segmentation, yet it suffers from critical limitations: mathematical intractability, substantial parameter requirements, and lack of clinical interpretability. We propose RadiomicNet, a novel two-stream hybrid architecture that enhances standard deep learning by integrating handcrafted radiomics features directly into the segmentation learning process. The key contribution is the Radiomics Attention Gate (RAG), which leverages Gr

As Large Language Models (LLMs) and agentic systems become integrated into real-world applications, ensuring their safety and security is critical. Guardrail systems that detect and block malicious instructions sent to and from an LLM are an essential component of AI security. However, researchers conducting black-box adversarial emulation against production AI systems often struggle to determine whether a guardrail block or an LLM rejection has occurred. This distinction is important because th

Autonomous AI agents increasingly depend on external knowledge stores, yet most retrieval pipelines provide relevance without durable guarantees of provenance, version identity, integrity, traceability, or point-in-time reconstruction. We formalize this as context governance and present ContextNest, an open specification and reference implementation for governed AI-consumable knowledge vaults. ContextNest does not replace Retrieval-Augmented Generation (RAG); it supplies the governance layer ben

Wave Function Collapse (WFC) is a widely used procedural content generation method that learns local adjacency constraints from example inputs to generate larger outputs. In this paper, we explore combining WFC with evolutionary search by evolving the small input examples used by WFC rather than directly evolving complete levels. In this approach, WFC acts as a genotype-to-phenotype mapping. The generated levels are then evaluated through domain-specific fitness functions. We evaluate the method

Ensuring model reliability in Explainable AI requires a global assessment of the hypothesis space. We propose a formal framework for the exhaustive analysis of optimal and near-optimal decision trees, called Algebraic Decision Tree Counting (ADTC). Inspired by Algebraic Model Counting (AMC) in knowledge representation, ADTC reformulates diverse analytical tasks, such as optimization, counting, and sampling, into a unified sum-of-products computation over a semiring $R$. While the hypothesis spac

Mirror Illusion Art is a novel reflection-conditioned 3D illusion where one object yields two target appearances (front and mirror). The task is formulated as inverse design from two target 2D images (front and mirror) to a printable 3D object with geometry and texture. Prior topology-driven and shadow-based approaches demand substantial manual effort, optimize shape only, and often yield non-smooth or incomplete geometry. To address these challenges, we propose AutoMIA, an automated Mirror Illu

Long-running adaptive intelligent agents face a structural tension between knowledge consolidation and information integrity. Memory consolidation is conventionally treated as an agent-changing operation: a model is fine-tuned, a prompt rewritten, a policy distilled, or a reflection appended to the context that governs future behaviour. In regulated autonomic deployment this is a liability because the agent operates under commitments and audit contracts that bind to a specific, cryptographically

Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in tasks such as molecular design and drug discovery. However, these models struggle to fully capture the visual representation of molecular structures, limiting their potential. While existing molecular vision-language models (VLMs) show promise, they still face challenges in structural alignment and lack the necessary topological modeling for accur

Online multimodal knowledge editing requires injecting a continual stream of visual-textual corrections into multimodal large language models (MLLMs) with bounded overhead and minimal disruption to unrelated behaviors. Existing editors mainly emphasize edit reliability and long-horizon stability, but rarely control the semantic boundary of each edit. Our pilot analyses of post-edit behaviors and internal neuronal activities reveal a scope gap behind reliable edits: instance-level success neither

Large language models (LLMs) are often asked to produce JSON conforming to a fixed schema, powering information extraction, tool calling, agentic planning, and knowledge-graph construction. Measuring how closely an output matches a gold reference is essential yet surprisingly hard: exact match is brittle, text similarity ignores structure, and an LLM judge is expensive, opaque, and non-deterministic. We address this with Object Aligner (OA), an open-source Python library that scores two JSON obj

We study the challenging problem of novel view video synthesis from single images or monocular videos. Existing methods, which operate under the assumption that pre-trained video models lack native novel view synthesis capability and enforce view alignment via camera conditioning, task-specific fine-tuning, or stepwise hard denoising guidance, often suffer from artifacts and compromised global scene consistency. In this paper, we introduce NeoMap, a novel training-free framework designed to loca

This work introduces AIriskEval-edu-db2, a new dataset designed to train and evaluate auditors based on LLMs for an explainable pedagogical risk assessment in instructional content for grades K-12. The dataset comprises 1,639 explanations from 170 curated ScienceQA questions, covering science, language arts, and social sciences. For each question, the dataset includes an explanation written by a human teacher alongside 11 explanations generated by LLM-simulated teacher profiles associated with d

Organizations increasingly make strategic decisions about AI systems whose behaviour, failure modes, and institutional effects cannot be fully known at design time. This technical report reframes strategic red teaming as a board-level governance discipline for testing the assumptions under which AI-enabled strategies are approved, funded, and supervised. The report proposes a six-component model for strategic red teaming in AI governance: an explicit assumption register, an adversarial mandate,

Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, largely treat semantics from large language models (LLMs) as auxiliary features or supervision, limiting their direct role in decision-making and constraining classification stability and robustness. To overcome this, we propose a semantic-aligned brain network framework that actively integrates LLM-derived semantics into the prediction process. Spec

Safety training for large language models (LLMs) is conducted predominantly in English, leaving uncertain how well safety mechanisms generalize to low-resource languages and mixed-language code-switching. We show that this creates an epistemic gap in which models confidently generate harmful responses for inputs that fall outside the distribution of their safety training. To study this phenomenon, we introduce STEER (Safety Targeted Embedding Exploit via Refinement), a gradient-guided attack tha

Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to temporal staleness, data contamination, and costly maintenance. We present MMBench-Live, a continuously evolving multimodal benchmark built by a multi-agent-driven automated pipeline. Our framework treats benchmark evolution as task-guided dataset construction, integrating structured benchmark specification, feedback-controlled real-time data acqui

Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing parameter-efficient fine-tuning (PEFT) methods such as LoRA ignore MoE routing dynamics, leading to suboptimal resource use. We propose EPnG, an adaptive prune-and-grow framework that reallocates LoRA capacity based on expert importance derived from router gate probabilities. EPnG prunes under-utilized experts and expands high-importance experts via ran

On-policy exploration is a crucial component for training robust Vision-Language Navigation agents, as it exposes the policy to a broader state distribution. However, such exploration inevitably leads to trajectories that deviate from expert demonstrations, resulting in a semantic mismatch between the executed visual stream and the original language instruction. In this work, we address this challenge by introducing Phi-Nav, a unified on-policy framework that leverages hindsight reasoning to ali

Open source software (OSS) is not homogeneous. A project's purpose, governance, and funding shape how its community forms, who contributes, and how the software is maintained, yet empirical research often samples OSS broadly and reports findings as if they held for open source as a whole. We argue that OSS comprises distinguishable sub-genres, and that the sub-genre a study samples bounds how far its findings generalize. Using a light, multi-source review that screens 3,925 unique papers, we syn

Public LLM leaderboards optimise for global average performance and do not capture the specific cognitive demands of financial-services work: a model that leads on MMLU-Pro may underperform on document-grounded compliance reasoning, and a coding leader may handle multi-turn customer interactions poorly. We present a meta-benchmarking framework that organises 452 publicly reported benchmarks into 41 O*NET Generalized Work Activities and aggregates those into 38 BIAN banking business domains spann

Existing robust preference optimization for language-model alignment mainly studies pairwise supervision and places robustness at the dataset, prompt, or preference-pair level. We instead study listwise preference optimization under ranking-label uncertainty: given a prompt and a candidate list, the observed ranking over that list may be ambiguous due to annotator inconsistency, near-ties, lossy rankwise feedback, or reward-model noise. We propose a pointwise total-variation robust Plackett--Luc

Finetuning a language model on documents that are explicitly annotated as fictional results in a model that still actually believes the documents' core claims, an effect known as Negation Neglect. In our evaluations, models trained on documents prefixed and suffixed with such annotations correctly identify the relevant claims as fictional only about 9% of the time. To address this, we introduce Goggles, a learned module that intervenes on the finetuning gradient rather than the data. During supe

Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-driven applications. Recently, large language model (LLM)-based data agents have emerged as a promising solution to automate data science workflows. However, the field lacks comprehensive benchmarks to rigorously evaluate t

Developing high-performance kernels for Neural Processing Units (NPUs) is a critical industry bottleneck, requiring developers to manually navigate implicit hardware constraints and strict memory hierarchies. While large language models offer immense automation potential, they fail catastrophically on NPUs due to a fundamental lack of hardware-specific priors. Naively transplanting code snippets from similar NPU kernels may pass the compiler, but it consistently triggers runtime crashes and perf

Vision-language-action models (VLAs) have recently advanced robotic manipulation, yet the effects of different robot-data pre-training paradigms remain difficult to compare because existing models often differ in architecture, data, action space, and evaluation protocol. We present VLAFlow (Vision-Language-Action Flow), a unified flow-matching framework for controlled comparison of VLA training objectives. Using a heterogeneous robot corpus, OXEMix, containing approximately 5,000 hours of data f

In today's data-driven world, students often struggle with interpreting visualisations due to limited visualisation literacy. Data comics have emerged as a promising medium to enhance engagement and understanding, but their educational value has seen little empirical examination, partly due to the effort required to create them. Recent advances in Generative AI (GenAI) offer a scalable solution to this challenge. We conducted a within-subjects study with 60 university students, comparing convent

Big Data & Society, Volume 13, Issue 3, July-September 2026. The regulation of artificial intelligence (AI) is a prominent issue in both the European Union (EU) and the United States of America, but with distinct approaches to the governance of this rapidly evolving field. The EU has developed a comprehensive ...

The emergence of the transformer architecture has ushered in a new era of possibilities, showcasing remarkable capabilities in generative tasks exemplified by models like GPT4o, Claude 3, and Llama 3. However, these advancements come with a caveat: predominantly trained on data gleaned from social media platforms, these systems inadvertently perpetuate societal biases and toxicity. Recognizing the paramount importance of AI Safety and Alignment, our study embarks on a thorough exploration throug

Carbon emissions drive climate change, and carbon credits mitigate climate deterioration and environmental damage while assisting organizations in managing their carbon footprint. Fully utilizing carbon credits remains challenging. This study enhances understanding of the engineering practices for carbon credits to develop responsible fintech solutions and provide insights for carbon emission management. We review the negative impacts of organizations’ strategy of evading carbon management throu

Legislative text translation requires unusually high fidelity because terminological substitutions, modal shifts, and discourse-level reformulations may alter rights, duties, or institutional meaning. While Large Language Models (LLMs) and multi-agent systems have advanced general-purpose translation, their probabilistic generation processes remain weakly aligned with the constrained interpretive conditions of statutory language. Existing agentic frameworks, which primarily rely on horizontal co

Language models are increasingly used to quantify cultural phenomena, but what makes such measurement distinctively cultural? This paper argues that NLP work on culture is a material-discursive practice: the apparatus -- model, data, annotation, evaluation -- participates in constituting the cultural reality it measures, rather than passively recording it. Drawing on Karen Barad's concept of the agential cut -- the contingent boundary between phenomenon and instrument -- I show that the apparatu

Distributed machine learning enables collaborative model training without centralizing data, but it also exposes learning processes to privacy leakage and malicious manipulation. Existing defenses typically address these threats in isolation and are often tailored to specific learning paradigms or model architectures, limiting their applicability in realistic deployments. In particular, federated learning and decentralized learning exhibit distinct adversarial surfaces that are rarely addressed

Restoring archival film remains a fundamentally challenging problem due to the absence of paired training data and the lack of standardized evaluation benchmarks. Pristine versions of deteriorated footage are physically unrecoverable, requiring supervised methods to rely on synthetic data that often fail to capture the complex, temporally coherent nature of real film degradation. At the same time, existing real-world datasets are limited in scale, quality, and accessibility, hindering reliable e

Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored. Electricity price forecasting (EPF) presents a challenging testbed due to complex temporal dependencies, distributional shifts, and strong reliance on structural and contextual information. We propose a two-dataset-benchmarking framework for EPF to mitigate contamination risk and enable fair evaluation of TSFMs. We exam