arXiv:2512.12548v1 Announce Type: new Abstract: Patch foraging involves the deliberate and planned process of determining the optimal time to depart from a resource-rich region and investigate potentially more beneficial alternatives. The Marginal Value Theorem (MVT) is frequently used to characterize this process, offering an optimality model for such foraging behaviors. Although this model has been widely used to make predictions in behavioral ecology, discovering the computational mechanisms that facilitate the emergence of optimal patch-foraging decisions in biological foragers remains under investigation. Here, we show that artificial foragers equipped with learned world models naturally converge to MVT-aligned strategies. Using a model-based reinforcement learning agent that acquires a parsimonious predictive representation of its environment, we demonstrate that anticipatory capabilities, rather than reward maximization alone, drive efficient patch-leaving behavior. Compared with standard model-free RL agents, these model-based agents exhibit decision patterns similar to many of their biological counterparts, suggesting that predictive world models can serve as a foundation for more explainable and biologically grounded decision-making in AI systems. Overall, our findings highlight the value of ecological optimality principles for advancing interpretable and adaptive AI.

arXiv:2512.12652v1 Announce Type: new Abstract: This paper introduces the concept of value awareness in AI, which goes beyond the traditional value-alignment problem. Our definition of value awareness presents us with a concise and simplified roadmap for engineering value-aware AI. The roadmap is structured around three core pillars: (1) learning and representing human values using formal semantics, (2) ensuring the value alignment of both individual agents and multiagent systems, and (3) providing value-based explainability on behaviour. The paper presents a selection of our ongoing work on some of these topics, along with applications to real-life domains.

arXiv:2512.12500v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm's opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two large-scale experiments (623 lay people; 153 primary care physicians, PCPs) combining a fairness-based diagnosis AI model and different XAI explanations to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. AI assistance balanced across skin tones improved accuracy and reduced diagnostic disparities. However, LLM explanations yielded divergent effects: lay users showed higher automation bias - accuracy boosted when AI was correct, reduced when AI erred - while experienced PCPs remained resilient, benefiting irrespective of AI accuracy. Presenting AI suggestions first also led to worse outcomes when the AI was incorrect for both groups. These findings highlight XAI's varying impact based on expertise and timing, underscoring LLMs as a "double-edged sword" in medical AI and informing future human-AI collaborative system design.

arXiv:2512.12620v1 Announce Type: cross Abstract: We study syllogistic reasoning in LLMs from the logical and natural language perspectives. In process, we explore fundamental reasoning capabilities of the LLMs and the direction this research is moving forward. To aid in our studies, we use 14 large language models and investigate their syllogistic reasoning capabilities in terms of symbolic inferences as well as natural language understanding. Even though this reasoning mechanism is not a uniform emergent property across LLMs, the perfect symbolic performances in certain models make us wonder whether LLMs are becoming more and more formal reasoning mechanisms, rather than making explicit the nuances of human reasoning.

arXiv:2512.12950v1 Announce Type: cross Abstract: Accurately mapping legal terminology across languages remains a significant challenge, especially for language pairs like Chinese and Japanese, which share a large number of homographs with different meanings. Existing resources and standardized tools for these languages are limited. To address this, we propose a human-AI collaborative approach for building a multilingual legal terminology database, based on a multi-agent framework. This approach integrates advanced large language models and legal domain experts throughout the entire process-from raw document preprocessing, article-level alignment, to terminology extraction, mapping, and quality assurance. Unlike a single automated pipeline, our approach places greater emphasis on how human experts participate in this multi-agent system. Humans and AI agents take on different roles: AI agents handle specific, repetitive tasks, such as OCR, text segmentation, semantic alignment, and initial terminology extraction, while human experts provide crucial oversight, review, and supervise the outputs with contextual knowledge and legal judgment. We tested the effectiveness of this framework using a trilingual parallel corpus comprising 35 key Chinese statutes, along with their English and Japanese translations. The experimental results show that this human-in-the-loop, multi-agent workflow not only improves the precision and consistency of multilingual legal terminology mapping but also offers greater scalability compared to traditional manual methods.

arXiv:2503.11950v4 Announce Type: replace-cross Abstract: The increasing integration of artificial intelligence (AI) in digital ecosystems has reshaped privacy dynamics, particularly for young digital citizens navigating data-driven environments. This study explores evolving privacy concerns across three key stakeholder groups-young digital citizens, parents/educators, and AI professionals-and assesses differences in data ownership, trust, transparency, parental mediation, education, and risk-benefit perceptions. Employing a grounded theory methodology, this research synthesizes insights from key participants through structured surveys, qualitative interviews, and focus groups to identify distinct privacy expectations. Young digital citizens emphasized autonomy and digital agency, while parents and educators prioritized oversight and AI literacy. AI professionals focused on balancing ethical design with system performance. The analysis revealed significant gaps in transparency and digital literacy, underscoring the need for inclusive, stakeholder-driven privacy frameworks. Drawing on comparative thematic analysis, this study introduces the Privacy-Ethics Alignment in AI (PEA-AI) model, which conceptualizes privacy decision-making as a dynamic negotiation among stakeholders. By aligning empirical findings with governance implications, this research provides a scalable foundation for adaptive, youth-centered AI privacy governance.

arXiv:2505.05019v2 Announce Type: replace-cross Abstract: The generation of synthetic clinical trial data offers a promising approach to mitigating privacy concerns and data accessibility limitations in medical research. However, ensuring that synthetic datasets maintain high fidelity, utility, and adherence to domain-specific constraints remains a key challenge. While hyperparameter optimization (HPO) improves generative model performance, the effectiveness of different optimization strategies for synthetic clinical data remains unclear. This study systematically evaluates four HPO objectives across nine generative models, comparing single-metric to compound metric optimization. Our results demonstrate that HPO consistently improves synthetic data quality, with Tab DDPM achieving the largest relative gains, followed by TVAE (60%), CTGAN (39%), and CTAB-GAN+ (38%). Compound metric optimization outperformed single-metric objectives, producing more generalizable synthetic datasets. Despite improving overall quality, HPO alone fails to prevent violations of essential clinical survival constraints. Preprocessing and postprocessing played a crucial role in reducing these violations, as models lacking robust processing steps produced invalid data in up to 61% of cases. These findings underscore the necessity of integrating explicit domain knowledge alongside HPO to generate high-quality synthetic datasets. Our study provides actionable recommendations for improving synthetic data generation, with future work needed to refine metric selection and validate findings on larger datasets.

arXiv:2508.09219v2 Announce Type: replace-cross Abstract: Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by these technologies. In this paper, we present a mixed-methods survey study - combining statistical and qualitative analyses - to examine the ethical perceptions, practices, and knowledge of individuals involved in various AI development roles. Our survey comprises 414 participants from 43 countries, representing various roles such as AI managers, analysts, developers, quality assurance professionals, and information security and privacy experts. The results reveal varying degrees of familiarity and experience with AI ethics principles, government initiatives, and risk mitigation strategies across roles, regions, and other demographic factors. Our findings underscore the importance of a collaborative, role-sensitive approach that involves diverse stakeholders in ethical decision-making throughout the AI development lifecycle. We advocate for developing tailored, inclusive solutions to address ethical challenges in AI development, and we propose future research directions and educational strategies to promote ethics-aware AI practices.

arXiv:2510.02967v3 Announce Type: replace-cross Abstract: This paper presents the development and evaluation of a Retrieval-Augmented Generation (RAG) system for querying the United Kingdom's National Institute for Health and Care Excellence (NICE) clinical guidelines using Large Language Models (LLMs). The extensive length and volume of these guidelines can impede their utilisation within a time-constrained healthcare system, a challenge this project addresses through the creation of a system capable of providing users with precisely matched information in response to natural language queries. The system's retrieval architecture, composed of a hybrid embedding mechanism, was evaluated against a corpus of 10,195 text chunks derived from three hundred guidelines. It demonstrates high performance, with a Mean Reciprocal Rank (MRR) of 0.814, a Recall of 81% at the first chunk and of 99.1% within the top ten retrieved chunks, when evaluated on 7901 queries. The most significant impact of the RAG system was observed during the generation phase. When evaluated on a manually curated dataset of seventy question-answer pairs, RAG-enhanced models showed substantial gains in performance. Faithfulness, the measure of whether an answer is supported by the source text, was increased by 64.7 percentage points to 99.5% for the RAG-enhanced O4-Mini model and significantly outperformed the medical-focused Meditron3-8B LLM, which scored 43%. Clinical evaluation by seven Subject Matter Experts (SMEs) further validated these findings, with GPT-4.1 achieving 98.7% accuracy while reducing unsafe responses by 67% compared to O4-Mini (from 3.0 to 1.0 per evaluator). This study thus establishes RAG as an effective, reliable, and scalable approach for applying generative AI in healthcare, enabling cost-effective access to medical guidelines.

arXiv:2510.12859v2 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) has emerged as both a continuation of historical technological revolutions and a potential rupture with them. This paper argues that AI must be viewed simultaneously through three lenses: \textit{risk}, where it resembles nuclear technology in its irreversible and global externalities; \textit{transformation}, where it parallels the Industrial Revolution as a general-purpose technology driving productivity and reorganization of labor; and \textit{continuity}, where it extends the fifty-year arc of computing revolutions from personal computing to the internet to mobile. Drawing on historical analogies, we emphasize that no past transition constituted a strict singularity: disruptive shifts eventually became governable through new norms and institutions. We examine recurring patterns across revolutions -- democratization at the usage layer, concentration at the production layer, falling costs, and deepening personalization -- and show how these dynamics are intensifying in the AI era. Sectoral analysis illustrates how accounting, law, education, translation, advertising, and software engineering are being reshaped as routine cognition is commoditized and human value shifts to judgment, trust, and ethical responsibility. At the frontier, the challenge of designing moral AI agents highlights the need for robust guardrails, mechanisms for moral generalization, and governance of emergent multi-agent dynamics. We conclude that AI is neither a singular break nor merely incremental progress. It is both evolutionary and revolutionary: predictable in its median effects yet carrying singularity-class tail risks. Good outcomes are not automatic; they require coupling pro-innovation strategies with safety governance, ensuring equitable access, and embedding AI within a human order of responsibility.

Enterprise leaders are pressing ahead with artificial intelligence, even as some early results remain uneven. Reporting from the Wall Street Journal and Reuters shows that most CEOs expect AI spending to keep rising through 2026, despite difficulty tying those investments to clear, enterprise-wide returns.

The tension highlights where many organisations now sit in their AI journey. The technology has moved beyond trials and proofs of concept, but it has yet to settle into a reliable source of value. Companies are operating in an in-between phase, where ambition, execution, and expectations are all under strain at the same time.

Spending continues, even as returns lag

AI budgets have climbed steadily in large enterprises over the past two years. Competitive pressure, board oversight, and fear of being left behind have all played a role. At the same time, executives are more open about the limits they are seeing. Gains often show up in pockets rather than in the business, pilots fail to spread, and the cost of connecting AI systems to existing tools keeps rising.

A Wall Street Journal survey of senior executives found that most CEOs see AI as central to long-term competitiveness, even if short-term benefits are hard to measure. For many, AI no longer feels optional. It is treated as a capability that must be developed over time, rather than a project that can be paused if results disappoint.

That view helps explain why spending remains steady. Leaders worry that cutting back now could weaken their position later, especially as rivals improve how they use the technology.

Why pilots struggle to scale

One of the main barriers to stronger returns is the jump from experimentation to day-to-day use. Many organisations have launched AI pilots in different teams, often without shared rules or coordination. While these efforts can generate insight and interest, few translate into changes that affect the wider business.

Reuters has reported that companies trying to scale AI frequently run into issues with data quality, system links, security controls, and regulatory requirements. The problems are not only technical, but reflect how work is organised. Responsibility is often split in teams, ownership is unclear, and decisions slow down once projects touch legal, risk, and IT functions.

The result is a pattern of heavy spending on trials, with limited progress toward systems that are embedded in core operations.

Infrastructure costs reshape the equation

The cost of infrastructure is also weighing on AI returns. Training and running models demands large amounts of computing power, storage, and energy. Cloud bills can rise quickly as use grows, while building on-site systems requires upfront investment and long planning cycles. Executives cited by Reuters have warned that infrastructure costs can outpace the benefits delivered by AI tools, particularly in the early stages. This has led to tough choices: whether to centralise AI resources or leave teams to experiment on their own; whether to build in-house systems or rely on vendors; and how much waste is acceptable while capabilities are still forming.

In practice, these decisions are shaping AI strategy as much as model performance or use-case selection.

AI governance moves to the centre of CEO decision-making

As AI spending increases, so does scrutiny. Boards, regulators, and internal audit teams are asking harder questions. In response, many organisations are tightening control. Decision rights are shifting toward central teams, AI councils are becoming more common, and projects are being linked more closely to business priorities.

The Wall Street Journal reports that companies are moving away from loosely connected experiments toward clearer goals, measures, and timelines. This can slow progress, but it reflects a growing belief that AI should be managed with the same discipline as other major investments.

The shift marks a change in how AI is treated. It is no longer a side effort or a curiosity but is being brought into existing operating and risk structures.

Expectations are being reset, not abandoned

Importantly, the persistence of AI spending does not signal blind optimism. Instead, it reflects a reset in expectations. CEOs are learning that AI rarely delivers immediate, sweeping returns. Value tends to emerge gradually, as organisations adjust workflows, retrain staff, and refine data foundations.

Rather than abandoning AI initiatives, many enterprises are narrowing their focus. They are prioritising fewer use cases, demanding clearer ownership, and aligning projects more closely with business outcomes. The re-calibration may reduce short-term excitement, but it improves the likelihood of sustainable returns.

What CEO AI strategy signals for 2026 planning

For organisations shaping their plans for 2026, the message for every CEO is not to retreat from AI, but to pursue it with more care as AI strategies mature. Ownership, governance, and realistic timelines matter more than headline spending levels or bold claims.

Those most likely to benefit are treating AI as a long-term shift in how the organisation works, not a quick route to growth. In the next phase, advantage will depend less on how much is spent and more on how well AI fits into everyday operations.

(Photo by Ambre Estève)

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The post CEOs still betting big on AI: Strategy vs. return on investment in 2026 appeared first on AI News.

npj Digital Medicine, Published online: 15 December 2025; doi:10.1038/s41746-025-02218-5

H&E-based MSI/MMR testing with AI in colorectal cancer: a multi-centred blinded evaluation

npj Digital Medicine, Published online: 15 December 2025; doi:10.1038/s41746-025-02230-9

A randomized clinical trial of app cognitive behavior therapy vs. HealthWatch for obsessive compulsive disorder

arXiv:2503.05860v3 Announce Type: replace-cross Abstract: Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for tasks such as code generation and bug repair. However, this proliferation has led to major challenges: (1) fragmented knowledge across tasks, (2) difficulty in selecting contextually relevant benchmarks, (3) lack of standardization in benchmark creation, and (4) flaws that limit utility. Addressing these requires a dual approach: systematically mapping existing benchmarks for informed selection and defining unified guidelines for robust, adaptable benchmark development. We conduct a review of 247 studies, identifying 273 AI4SE benchmarks since 2014. We categorize them, analyze limitations, and expose gaps in current practices. Building on these insights, we introduce BenchScout, an extensible semantic search tool for locating suitable benchmarks. BenchScout employs automated clustering with contextual embeddings of benchmark-related studies, followed by dimensionality reduction. In a user study with 22 participants, BenchScout achieved usability, effectiveness, and intuitiveness scores of 4.5, 4.0, and 4.1 out of 5. To improve benchmarking standards, we propose BenchFrame, a unified framework for enhancing benchmark quality. Applying BenchFrame to HumanEval yielded HumanEvalNext, featuring corrected errors, improved language conversion, higher test coverage, and greater difficulty. Evaluating 10 state-of-the-art code models on HumanEval, HumanEvalPlus, and HumanEvalNext revealed average pass-at-1 drops of 31.22% and 19.94%, respectively, underscoring the need for continuous benchmark refinement. We further examine BenchFrame's scalability through an agentic pipeline and confirm its generalizability on the MBPP dataset. All review data, user study materials, and enhanced benchmarks are publicly released.

arXiv:2509.12421v2 Announce Type: replace-cross Abstract: The rapid adoption of foundation models (e.g., large language models) has given rise to promptware, i.e., software built using natural language prompts. Effective management of prompts, such as organization and quality assurance, is essential yet challenging. In this study, we perform an empirical analysis of 24,800 open-source prompts from 92 GitHub repositories to investigate prompt management practices and quality attributes. Our findings reveal critical challenges such as considerable inconsistencies in prompt formatting, substantial internal and external prompt duplication, and frequent readability and spelling issues. Based on these findings, we provide actionable recommendations for developers to enhance the usability and maintainability of open-source prompts within the rapidly evolving promptware ecosystem.

arXiv:2512.10041v2 Announce Type: replace-cross Abstract: Modern deep learning methods have achieved impressive results across tasks from disease classification, estimating continuous biomarkers, to generating realistic medical images. Most of these approaches are trained to model conditional distributions defined by a specific predictive direction with a specific set of input variables. We introduce MetaVoxel, a generative joint diffusion modeling framework that models the joint distribution over imaging data and clinical metadata by learning a single diffusion process spanning all variables. By capturing the joint distribution, MetaVoxel unifies tasks that traditionally require separate conditional models and supports flexible zero-shot inference using arbitrary subsets of inputs without task-specific retraining. Using more than 10,000 T1-weighted MRI scans paired with clinical metadata from nine datasets, we show that a single MetaVoxel model can perform image generation, age estimation, and sex prediction, achieving performance comparable to established task-specific baselines. Additional experiments highlight its capabilities for flexible inference. Together, these findings demonstrate that joint multimodal diffusion offers a promising direction for unifying medical AI models and enabling broader clinical applicability.

MiTEs are myeloid-targeted immunocytokine prodrugs that block TREM2+ tumor-associated macrophages while activating cytotoxic lymphocytes via TME-specific IL-2 activity, eliciting strong anti-tumor efficacy in preclinical models with minimal systemic toxicity.