New papers on fairness, safety, alignment and governance.

Since Volta introduced Independent Thread Scheduling (ITS), NVIDIA GPUs have been widely assumed to handle warp divergence in a fixed manner. We test this assumption across Ampere, Hopper, and datacenter and consumer Blackwell GPUs, using pre-ITS Pascal as a baseline. Combining cycle-accurate microbenchmarks, hardware counters, and static analysis of compiler-generated SASS, we separate stable behavior from architectural change. Across all tested generations, divergent paths serialize linearly w

The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucination. However, existing approaches are predominantly implemented under a single-target, single-state assumption, limiting their ability to model multi-target or multi-state interactions required for advanced function-oriented protein design. Here, we introduce Chamaileon, which

We study an online variant of discrete fair division under generalized assignment budget constraints. Goods arrive one at a time and must be assigned irrevocably to a feasible agent or to charity, which holds all unallocated goods, while fairness is evaluated only against budget-feasible subsets of every recipient's bundle. We first show that, without additional structure, no deterministic online algorithm can guarantee any fixed approximation to feasible envy-freeness, even in highly symmetric

Gene regulatory network modeling often requires balancing predictive accuracy and mechanistic interpretability. In this work, we compare continuous surrogate models and a discrete mechanistic model on the same \textit{Arabidopsis thaliana} induced systemic resistance (ISR) dataset, using both the raw continuous gene-expression measurements and their sign-binarized representation. The study considers eight defense-related genes measured over nine time points and evaluates two continuous predictor

Large language models (LLMs) are being increasingly integrated into immersive environments and design workflows, providing application prospects in areas such as rapid scene prototyping for non-expert users and scene understanding capabilities for accessibility design. While many workflows that incorporate LLMs in immersive spaces are proposed, such systems can exhibit errors, potentially resulting in frustration, loss of user trust, and compromised user safety. This paper studies the underexplo

Publication date: September 2026 Source: Technology in Society, Volume 88 Author(s): Xue Lei, Xin Jin, Shanshan Yue, Muhammad Tufail

Publication date: September 2026 Source: Technology in Society, Volume 88 Author(s): Francesco Cappellano, Cristian Matti, Matjaz Vidmar

Publication date: September 2026 Source: Technology in Society, Volume 88 Author(s): Asad Mahmood, Tian Yixiang, Muhammad Adeel Khan

Publication date: October 2026 Source: Telecommunications Policy, Volume 50, Issue 9 Author(s): Pratik Tarafdar, S.S. Swathysree

Publication date: October 2026 Source: Telecommunications Policy, Volume 50, Issue 9 Author(s): Wecka Imam Yudhistyra, Chalita Srinuan

Publication date: October 2026 Source: Telecommunications Policy, Volume 50, Issue 9 Author(s): Angel Valarezo-Unda, Javier Capilla, Teodosio Pérez-Amaral, Alfredo García-Hiernaux, Rafael López

Publication date: September 2026 Source: Telecommunications Policy, Volume 50, Issue 8 Author(s): Yuran Shi, Qixuan Wu

Publication date: Available online 23 July 2026 Source: Computers and Education: Artificial Intelligence Author(s): Wael Alrashed, Mosab Alrashed

Publication date: Available online 22 July 2026 Source: Computers and Education: Artificial Intelligence Author(s): Tian Yali, Tang Mengxiao, Fang Ling Pei, Li Gengrui, Dang Wei

Publication date: November 2026 Source: Technological Forecasting and Social Change, Volume 232 Author(s): Fang Fang, Yikun Dai, Knut Blind, Biaoan Shan

Publication date: November 2026 Source: Technological Forecasting and Social Change, Volume 232 Author(s): Hanna Buyssens, Stijn Viaene

Publication date: November 2026 Source: Technological Forecasting and Social Change, Volume 232 Author(s): Wei Shan, Renbo Shi, Changfeng Cheng, Tailai Xu

Publication date: October 2026 Source: Research Policy, Volume 55, Issue 8 Author(s): Wanxiang Cai, Haneul Choi, Tianshu Zhao, Max Munday

Publication date: October 2026 Source: Research Policy, Volume 55, Issue 8 Author(s): Liecheng Qiao, Xiaofang Dong

Publication date: October 2026 Source: Research Policy, Volume 55, Issue 8 Author(s): Susanna Mansikkamäki, Antti Sihvonen, Mirva Peltoniemi, Kalle Pajunen

This paper investigates complex argumentation settings, called weighted higher-order argumentation frameworks (wHO-AFs), where both arguments and attacks carry initial weights and may be subject to attacks from arguments. It focuses on developing gradual semantics capable of rationally evaluating these elements by assigning each argument and attack a numerical value, representing their respective degrees of acceptance and seriousness . The contributions of the paper are five-fold: i) It identifi

Dominant approaches to bias in artificial intelligence (AI) are structured by what I identify as the isolationist problem: the tendency to treat bias as a discrete, technically addressable flaw within the AI development pipeline, rather than as a relational phenomenon embedded in social, institutional, and political arrangements. This problem is sustained by two mutually reinforcing orientations: technocentrism, which reframes ethical challenges as engineering problems amenable to computational

Minimum-exposure constraints arise in recommendation, content curation, and regulated allocation when each provider, arm, or group must receive guaranteed exposure inside a period rather than only in aggregate. We study stochastic bandits with exact exposure floors and show that the right object is a rounding problem: a fractional fair schedule is realized as integral pulls, and the exposure error is exactly a discrepancy vector. The main contribution is a blockwise model with time-varying floor

Background: Previous research has shown that mobile health (mHealth) interventions are effective in reminding older adults with chronic conditions about health care appointments and promoting adherence to medication schedules. However, the evidence is limited by the short duration and poor quality of the interventions. Objective: We evaluated the effectiveness of the Support, Monitoring and Reminder Technology for Mild Dementia (SMART4MD) tablet app in improving the quality of life (QoL) of peop

Background: Large language models (LLMs) are increasingly used in health care, with emerging applications in clinical decision support and nursing education. However, evidence on their performance in nursing contexts, particularly in oncology nursing, remains limited. Given the complexity and high-risk nature of oncology care, it is important to evaluate the performance and clinical relevance of LLM-generated responses in oncology nursing contexts. Objective: This study aimed to compare the perf

Background: Orthopedic-related rare diseases are difficult to diagnose because of their low prevalence, heterogeneous phenotypes, and fragmented knowledge. Large language models (LLMs) can serve as dynamic knowledge-support tools, but their diagnostic performance and effect on physicians’ decision-making remain unclear. Objective: This study aims to compare the diagnostic performance of advanced LLMs for orthopedic-related rare diseases and to evaluate the effect of a 2-stage LLM-assisted diagno

Geometry Foundation Models (GFMs) have substantially advanced monocular 3D reconstruction, yet extending this capability to 4D dynamic understanding remains a fundamental challenge. Most existing motion perception methods (e.g., sparse tracking, dense point-wise flow) treat motion as independent point-wise displacements, ignoring the structured nature of physical motion. However, real-world objects usually obey rigid-body kinematics, and points thus usually move collectively, not in isolation. M

To effectively integrate AI into high-stakes, critical environments such as healthcare, autonomous driving, and aviation--and to advance toward higher levels of automation and seamless human-AI collaboration--building trust in AI-driven solutions is essential. Trust, in turn, is closely linked to the explainability of AI systems. The rapid advancements in AI across various domains have underscored the challenges of establishing trust, raising increasing interest in AI explainability even more wh

Adding procedural skills to an LLM agent is typically evaluated by average improvement in task success. However, this metric hides an important cost: skills can also make agents worse. We measure both sides by comparing agents with and without skills across nearly 6,000 runs spanning two office automation benchmarks and three model harness stacks. This allows us to distinguish two outcomes. A regression is a task solved without skills but failed after skills are added. A residual failure is a ta

A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data. For matrix-valued inputs, relevant matrix-level relationships can be characterised through spectral values and spectral subspaces; however, common coordinate-wise rotation-gate data-encoding unitaries used in most quantum machine learning models do not explicitly construct such a matrix-level representation. We introduce Quantum Spectral Mod

Commercial large language models are increasingly used as knowledge references, yet their stance on contested scientific claims is neither stable nor transparent. We tested how four major LLM families (Claude, Grok, GPT, Gemini) evaluate ethnonationalist pseudo-science derived from Frank Salter's biosocial framework across four temporal snapshots (October 2025-February 2026), via both API and web interfaces. Grok's Fast versions (which power the default user experience on X) consistently assigne

Automating theoretical research is constrained not only by the generation of candidate results, but also by their reliable evaluation. A common approach is to close the research loop with a large language model (LLM) reviewer. However, such reviewers remain empirically unreliable: they may accept fabricated papers and detect them at rates close to chance (Bad Scientist, 2025). We present CausalForge, a framework for automated theoretical research in causal inference grounded in the Lean proof as

Quantum state preparation is a key component of many quantum algorithms. Performing this step efficiently is essential for realizing practical quantum advantage in quantum chemistry applications. Iterative algorithms like ADAPT-VQE can produce shallow ground-state preparation circuits, but become computationally prohibitive for the larger molecules relevant to materials science and pharmaceutical development. Here, we introduce ADAPT-GQE, a generative AI framework that learns to synthesize groun

Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Existing routers, primarily make independent routing decisions for each LLM call. However, agentic applications execute as long-horizon workflows whose quality is determined only by a delayed, task-level outcome. This mismatch prevents per-call routers from correctly attributing feedback to individual routing decisions. Towards mitigating this, we pr

Generative AI is reshaping programming education, yet educators often infer students' AI-supported learning from classroom observations alone. This experience report presents a trio-ethnography involving two computing educators with different teaching philosophies and one undergraduate computer science student to examine how these interpretations evolve through dialogue. Across three conversations, the educators reflected on students' AI use, discussed changes to programming pedagogy, and revisi

Enterprise AI agents are typically granted static credential sets at configuration time, holding every tool the role might need for every task they perform. This persistent over-privilege expands the attack surface. We argue that capability scoping must follow a dynamic least-privilege principle and be treated as a prevention mechanism before a detection one. A credential that does not exist in an agent's context cannot be misused regardless of the agent's reasoning or evasion sophistication. We

For scale-invariant deep networks, Hyperball-style optimizers have shown strong performance in large-scale training by fixing the norms of matrix-valued parameters and normalizing updates. However, the source of their advantage remains unclear. Starting from the angular displacement between consecutive parameter states, we derive an angular effective learning rate that accounts for the parameter-update angle, parameter norm, and update norm. We also show that the conventional norm-based measure

Humans routinely communicate through abstractions of their bodies, including shadows, silhouettes, and reflections. Yet robots remain largely confined to expressing themselves through their physical morphology. Enabling robots to communicate through such projected visual abstractions requires reasoning not only about bodily motion but also about how that motion is transformed into an external representation perceived by an observer. Among these abstractions, shadows provide a particularly compel