How humans grow and maintain abstract knowledge from the sparse, streaming noisy data of experience is a longstanding challenge in cognitive science. Any computational account must satisfy at least three desiderata: It must be (1) data-efficient and compute-efficient, (2) capture gradations of uncertainty to support intelligent inquiry and information gathering, and (3) be flexible enough to mentally represent the endless range of concepts people can learn and think about. Here we introduce a computational model that captures these three properties, by encoding symbolic knowledge as mental programs that combine natural language with source code, and sequentially inferring mental programs using LLM-guided Bayesian learning algorithms. Across a range of behavioral studies this model successfully reproduces quantitative signatures of human inductive learning and active inquiry, such as anchoring, garden-pathing, and other effects. In contrast, pure LLMs and classic Bayesian models either fail at the underlying task, or do not reproduce human behavior, or succeed only at exorbitant computational cost. These results suggest that one way humans continually grow their knowledge is by mentally representing many hypotheses spanning language-like and program-like representations, then revising those hypotheses to approximate Bayesian updates, while a bottom-up neural mechanism (an LLM) makes inference both tractable and learnable.
Wasu Top Piriyakulkij, Sam Acquaviva, Cassidy Langenfeld et al.· 0 citations
Information sharing can improve a pooled estimate while eliminating independent rescue actions. This paper separates those effects in exact finite discovery models. A centralized action-budget profile shows that equal one-person accuracy can coexist with different portfolio values. Under a registered incremental-sharing protocol, a sharing step improves discovery exactly when pooled residual error contracts faster than an independent rescue attempt. Exact bounded registries exhibit compression, aggregation, neutral curves, and a bounded zero mixed class. In a two-agent Bayesian game with a hidden mixture of common and independent signal sources, the registered selected equilibrium yields a strict positive sharing interval at signal accuracy 3/5, while alternative equilibria show that the result is selection-dependent rather than universal. The models are synthetic and finite; no human or organizational data are used.
Statutes are increasingly parsed by machines before people read them, and the parsers disagree: on Missouri's statutes, two independently written extractors diverge on numeric-threshold presence at a false-negative rate of 0.43. We ask what formal logic survives such noise. We build a passive survival certificate for the Duquenne-Guigues implication basis of machine-extracted statutory contexts: per-attribute inter-extractor disagreement is measured, replayed against the basis in 1,000 Monte Carlo trials, and an implication is certified only when a one-sided Wilson 95% lower bound on survival reaches 0.95; every certified implication carries premise spans and a minimal counterexample. On 29,365 Missouri sections and 502 Indian central-Act sections, the preregistered held-out gate passes (10 statute families across 7 Titles exact; 16 across 11 with 5% tolerance), yet under one globally deployed error model 93.2% of held-out chapters fall below the informativeness floor, and a 2x2 factorial assigns that to calibration-rate transfer, not selection. The certificate is usable but fragile: deploy it per-chapter-calibrated or error-tolerant. Code, data products, and the audit trail, including one retracted claim, are released.
With the development of artificial intelligence (AI), the landscape of meta-ethics, which has largely centred on human ethics, faces pressures that may significantly reconfigure it. In particular, if future AI systems were to exhibit sufficiently integrated capacities for moral reasoning, moral intentionality, and moral reflection, novel meta-ethical questions would arise concerning what I call "AI's own ethics", as distinct from ethical principles merely imposed on AI by human designers. This paper offers a conditional and methodological framework for identifying the questions that would emerge if such AI systems were to arise. On that basis, the paper distinguishes four domains of meta-ethical inquiry in the era of AI: questions about the nature of human ethics from the human perspective; questions about the nature of AI's own ethics from the human perspective; questions about the nature of human ethics from the AI perspective; and questions about the nature of AI's own ethics from the AI perspective. The paper then considers how some existing mainstream meta-ethical theories (such as cognitivism and non-cognitivism, error theory and success theory, relativism, and objective realism) might illuminate these domains, while arguing that many familiar human-centred formulations of those theories may not transfer straightforwardly to AI cases without substantial revision. The overall conclusion is that the emergence of AI's own ethics would place significant pressure on current frameworks and may require substantial refinement, reconstruction, or reconceptualisation.
Shang Lu· 0 citations
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Frontier large language models can often recognize when they are being evaluated, a capability known as evaluation awareness. If models behave differently in evaluations than in deployment, this undermines the validity of evaluation results, which are a crucial component of current AI safety frameworks. We introduce EvalDetectBench, an open pipeline and benchmark for measuring evaluation awareness that works with any Inspect-compatible evaluation, allowing practitioners to test against current and future benchmarks. EvalDetectBench ships with a newly curated transcript suite covering current frontier system-card evaluations and diverse deployment sources. The benchmark serves two purposes: measuring how reliably frontier LLMs recognize that they are being evaluated, and assessing how detectable individual benchmarks are as evaluations. We identify two methodological choices in the existing literature that introduce systematic bias: the identity of the model that generated the deployment transcripts accounts for 11.25% of measurement variance and can reorder model rankings; and elicitation prompts selected for high performance on one model can perform near chance on others. EvalDetectBench corrects for both via per-model probe calibration and a stratified generator-harmonisation procedure.
Abstract Artificial intelligence (AI) is rapidly transforming histopathology, with applications ranging from workflow optimisation and quality assurance to tumour diagnosis, grading, biomarker assessment and estimation of prognosis. While numerous AI algorithms have demonstrated promising analytical and clinical performance, pathology laboratories are increasingly adopting commercially available AI systems with regulatory-approval rather than developing their own algorithms. Existing guidance largely focuses on AI development, validation and regulatory approval, with comparatively little practical direction on the local verification, governance and ongoing assurance required for safe routine clinical implementation. This paper proposes a practical framework for the clinical implementation of AI specifically within pathology laboratories. Rather than addressing AI development, it focuses on the responsibilities of laboratories adopting established AI systems into clinical practice. The framework distinguishes AI applications according to their intended clinical function, recognising that diagnostic applications, biomarker evaluation, workflow optimisation and generative AI applications require different implementation, verification, governance and quality assurance strategies. It further distinguishes algorithm validation, local verification and continuous assurance as complementary stages of implementation and advocates a function-based, risk-proportionate approach integrated within existing laboratory quality management systems. Practical recommendations are provided for workflow integration, interoperability, human oversight, user competency, performance monitoring, incident management, software updates and proportionate re-verification throughout the AI operational lifecycle. By extending implementation beyond regulatory approval, this guidance complements existing AI development and regulatory frameworks rather than replacing them. It provides a practical governance framework for pathology laboratories, professional organisations, accreditation bodies, and healthcare providers to support the safe, standardised, and sustainable integration of AI into routine histopathology while maintaining diagnostic quality, patient safety, and clinical governance.
Emad A. Rakha, Jelle Wesseling, Anikó Kovács et al.· Archiv für Pathologische Ana...· 0 citations
Large language model (LLM)-based artificial intelligence is increasingly used in ethically consequential human decision-making, yet fully autonomous machine ethics remains unrealistic, motivating architectures that support rather than replace human ethical judgment. This study introduces the Cognitive–Reflective Equilibration Architecture (CREA), a cognitively grounded artificial moral advisor that operationalizes the Cognitive–Reflective Equilibration Model (CREM), in which reflective reasoning guides ethical judgment from intuitive cognition toward a more advanced equilibrium among competing values, drawing on Piaget and Rawls. CREA implements CREM’s 20-step process through four stage-aligned reasoning agents—Cognitive, Reflective, Equilibration, and Evaluation—coordinated via multi-LLM orchestration, in which auxiliary models independently explore principles, generate counterarguments, and score supporting and opposing considerations to externalize reflective deliberation. The architecture was empirically evaluated by comparing four configurations—single-agent, multi-agent, multi-LLM, and multi-LLM with knowledge- and reasoning-bank augmentation—across four indicators of advice quality using 500 matched execution units per configuration. All comparisons are system-internal: advice quality was scored by CREA’s own multi-LLM measurement pipeline rather than by human ethicists, so the findings reflect relative differences among architectures under LLM-based self-evaluation, not normative validity. Within that scope, distributing reflective reasoning across multiple models was associated with higher reason-giving (justifiability) and normative-alignment scores relative to simpler configurations. CREA therefore offers an empirically characterized, auditable advisor architecture whose potential to scaffold human ethical judgment remains a hypothesis for user-centered validation rather than a demonstrated outcome.
Chulmin Kim, Seongjin Ahn· Journal of Intelligence· 0 citations
Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020–2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.
Introduction Physical activity in a mixed-reality (MR) environment may be an attractive exercise; however, its intensity depends on application characteristics and the extent of whole-body involvement. One method of increasing exercise load during activities based primarily on upper-limb movements may be additional elastic resistance. The aim of this study was to assess the effects of elastic resistance applied to the upper limbs on exercise intensity, perceived exertion, enjoyment, and flow state during boxing in MR. Method The study included 36 university students, comprising 20 women (age: 22.25 ± 1.52 years) and 16 men (age: 22.00 ± 1.46 years). Each participant completed three 10-minute simulated boxing bouts against an artificial intelligence-controlled opponent in the Golden Gloves application using a Meta Quest 3 headset in mixed-reality mode. Trials were performed without resistance and with lower or higher elastic resistance generated on the participant's body using the WearBands system. The order of conditions was counterbalanced. Exercise intensity was assessed based on the percentage of maximum heart rate (%HR max ) and the Borg 6–20 scale. Enjoyment was assessed using the short Physical Activity Enjoyment Scale, whereas flow state was evaluated using the short Flow State Scale. Results Increasing elastic resistance resulted in a significant increase in mean heart rate, from 156.61 ± 15.26 beats/min without resistance to 164.97 ± 13.99 beats/min with lower resistance and 169.75 ± 13.42 beats/min with higher resistance ( p < 0.001; η 2 p = 0.63). A similar relationship was found for %HR max , which increased from 81.34 ± 7.83% to 85.68 ± 7.21% and 88.17 ± 6.96%, respectively ( p < 0.001; η 2 p = 0.63). Perceived exertion also increased with resistance, reaching 10.86 ± 2.32, 13.50 ± 2.13, and 15.83 ± 1.84 points, respectively ( p < 0.001; W = 0.84). No significant differences were found in enjoyment or flow state. Discussion The portable elastic resistance system for upper limbs progressively increased heart rate-based and perceived intensity during a 10-minute MR boxing bout without reducing enjoyment or flow in active university students. These findings suggest WearBands may practically manipulate MR exercise load; longitudinal studies are needed to assess safety, adherence, metabolic demand, and training effects.
Jacek Polechoński, Aleksandra Daniel, Małgorzata Dębska et al.· Frontiers in Sports and Acti...· 0 citations
This study explores the relationship between Artificial Intelligence (AI) and net-zero carbon buildings (NZCBs) and net-zero energy buildings (NZEBs) over the last decade. A thematic evolution has been observed in this research area, shifting from conventional optimization towards more advanced digital, intelligent, and decarbonized infrastructure. Co-occurrence, clustering, thematic evolution, network, and visualization justify this science mapping analysis at regular intervals (2015–2018, 2019–2022, and 2023–2026). Digital twins (DTs) have been identified as the dominant theme in strategic analysis, integrating Building Information Modeling (BIM), sensors, communication networks, and AI algorithms. In contrast, there has been the emergence of GIS as a complementary platform for extending AI applications beyond individual buildings to neighborhood, city, and regional scales through carbon mapping, life-cycle assessment, energy storage planning, and spatial decision-making. The analysis highlights AI as supporting technology rather than an isolated research theme, managing building information through digital twins and facilitating urban-scale decarbonization through GIS. The novelty of this study lies in proposing a dual framework that aligns digital twins and GIS as complementary implementation platforms for connecting AI with net-zero building objectives. The developed framework provides valuable insights into the intellectual structures creating intelligent, energy-efficient, and carbon-neutral built environments.
Quddus Tushar, Muhammed A. Bhuiyan, Ziyad Abunada et al.· Clean Technologies· 0 citations
Purpose: The global shift towards clean energy necessitates robust business strategies to address challenges related to intermittent supply, operational inefficiencies, and evolving market conditions. This study fills a gap in strategic management literature by exploring the role of Artificial Intelligence (AI) and Digital Transformation (DT) in enhancing sustainable business performance within renewable energy enterprises. Design/Methodology/Approach: Utilizing the Resource-Based View (RBV) and Dynamic Capabilities Theory, this study conducts a qualitative conceptual synthesis and systematic analysis of recent literature (2018–2026) to formulate an integrated strategic management framework. Findings: The study presents the AI-Driven Digital Transformation for Sustainable Energy (ADT-SE) framework. This model demonstrates that the alignment of technological drivers—such as predictive analytics, IoT, smart grids, and block chain—with core organizational capabilities significantly enhances financial performance, operational efficiency, and environmental stewardship. Practical Implications: The study offers renewable energy executives and policy planners a practical roadmap for capital allocation, digital risk management, and the development of cross-functional capabilities. Originality/Value: This research connects technical AI applications with corporate sustainability strategies within the Commerce and Management domain.
Dhananjay Rambhau Aundhekar, Dr. Shilpi Agarwal· International Research Journ...· 0 citations
Results for counterfactual image generation for the CRC-VAL-HE-7K dataset. A, Tissue-type classifier-guided counterfactuals of a dysplastic tile, generated by shifting its feature vector toward the predicted healthy colon mucosa class. Changes reflect what the model requires to flip the prediction, with increasing manipulation amplitude shown above each image. B, The t-SNE projection of the feature extractor’s latent space (training set), colored by class: adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), healthy colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium/dysplastic tissue (TUM). The features of dysplastic tile in A, initially near the boundary between TUM (aqua) and NORM (gray), shift toward the healthy cluster (pink points in the zoomed-in plot; gray area—arbitrary healthy mucosa zone) as manipulation amplitude increases. C, Morphologic feature prevalence in original and counterfactual image pairs. Horizontal bars show the percentage of image pairs in which at least one of four raters (three board-certified pathologists and one pathology trainee) identified each morphologic feature as present in the original (dark gray) or counterfactual (light gray) image. Right, Mean pairwise inter-rater agreement (Cohen κ) per feature, computed across all pairs and both directions combined. D, Counterfactuals of a healthy mucosa tile shifted toward dysplastic epithelium. Below, Pixel-level difference maps (darker regions indicate greater changes), showing the most change in gland regions, and SSIM values, which quantify the similarity between the original and manipulated tiles. Bottom, Segmented and classified nuclei: pink (epithelial), orange (connective tissue), blue (plasma), deep purple (lymphocytes), aqua (neutrophils), and green (eosinophils). E, Tile-level differences in cell type fractions between (left) real NORM tiles (n = 741) and synthetic (counterfactual images generated from TUM tiles, n = 1,233; full statistical details are provided in Supplementary Table S4). Right, Tile-level differences in cell-type fractions between real TUM tiles (n = 1,233) and synthetic (counterfactual images generated from NORM tiles, n = 741).
Laura Žigutytė, Tim Lenz, Tianyu Han et al.· 0 citations
