Tool-using LLM agents spend wall-clock time not only on model inference but also in serial action--observation turns, where each tool call, environment transition, and observation can delay subsequent decisions. We introduce \textbf{Speculative Macro Commit} (SMC), a runtime mechanism for a two-tier agent system: a large authoritative actor model produces the official trajectory, while a faster speculative drafter model continuously predicts and executes future action chains on an isolated environment snapshot. SMC mines recurring multi-action skeletons from training traces and stores them in a macro library used to match against action chains predicted by the drafter at runtime. When the actor's next tool call matches the first drafted action, SMC commits the remaining pre-executed draft steps, together with their observations, to the official trajectory. Using Qwen3.5-27B INT4 as the authoritative actor model and Qwen3.5-4B as the speculative drafter model, SMC matches the sequential agent's overall accuracy while reducing latency by 10.23\% over the Speculative Actions (SA) baseline and 18.59\% over sequential execution on the $\tau^2$-Bench Telecom subset. On AppWorld, SMC reduces wall time by 7.7\% over SA baseline and 44.9\% over sequential execution, with a small reduction in task completion. Overall, SMC provides a practical way to reuse multi-step speculative execution and reduce agent latency beyond single-step speculative actions. Our code is publicly available \href{https://github.com/zeyuliu1037/speculative-macro-commit}{\textcolor{magenta}{here}}.
Zeyu Liu, Souvik Kundu, Peter A. Beerel· 0 citations
We study a governed approach to enterprise analytics: a language model interprets the question, while deterministic policy selects and runs a pre-approved analytical program that returns both results and evidence. We show that this restriction can remain expressive within a defined analytical class, using relational operations plus aggregation, comparison, windows, ranking, and similarity. Fixed meaning, policy, data, and execution rules also make results replayable. Across 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B interpreted intent only and policy executed the approved program. None of 330 runtime-planning episodes matched the full answer-and-evidence contract across all test datasets; the policy-executed analyzer matched 110 of 110. This is a configuration-specific result, not evidence that runtime agents cannot succeed under other designs.
Artificial intelligence is changing the form of applied English materials from fixed paper sequences to adaptive learning systems that can diagnose learners, recommend tasks, and provide formative feedback. This paper studies the structure and application of a new practical English textbook driven by artificial intelligence. A five-layer architecture is proposed: knowledge mapping, learner profiling, task generation, feedback orchestration, and teacher-side governance. A prototype was tested on 186 non-English-major undergraduates for eight weeks of teaching. Compared with a static digital textbook, the proposed system increased the unit completion accuracy from 72.4% to 84.9%, raised the average score for speaking tasks by 10.8 points, and reduced the teacher's correction time by 31.6%. Therefore, an AI-driven textbook can maintain the stability of the curriculum while providing personalised learning paths, rich practice materials and traceable classroom data.
Ya Wang, Lei Zhang, Xueguang Yang et al.· 0 citations
This dissertation investigates the role of Leiblichkeit (“bodiliness”) in the theology of Karl Rahner (1904–1984), arguing that it constitutes a foundational hermeneutical category across the major domains of his thought. Although existing scholarship has recognized Rahner’s emphasis on the concrete and historical character of human existence, it has not adequately examined bodiliness as a theological category in its own right. Through close and systematic readings of Rahner’s early writings on spirituality, his major philosophical works, and selected later theological writings, this study addresses that lacuna by demonstrating that bodiliness provides a unifying interpretive framework for his treatments of spirituality, philosophy, grace, Christology, anthropology, ecclesiology, and eschatology. The dissertation is organized into two parts. The first explores the spiritual and philosophical foundations of bodiliness, establishing embodied existence as the ontological condition of mystical experience, human knowledge, and receptivity to divine revelation. The second traces its systematic theological implications, demonstrating that Rahner’s accounts of grace, Christ, the human person, the Church, and eschatological fulfilment are intrinsically grounded in historicity and embodied existence. The central thesis is that Leiblichkeit functions not merely as a recurring motif but as the constitutive horizon through which the human person exists in relation to the world, other persons and, ultimately God. By retrieving the theological significance of bodiliness, this study offers a more integrated interpretation of Rahner’s thought and opens avenues for interdisciplinary dialogue with philosophical anthropology, psychology, bioethics, feminist and ecological theology, and contemporary debates concerning artificial intelligence.
Philip Shube BAWE· Zenodo (CERN European Organi...· 0 citations
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The purpose of this study was to analyze patterns of artificial intelligence (AI) use and attitudes toward AI among students, faculty, and administrative staff at a large university specializing in teacher education. The analytical sample comprised 1809 students, 250 faculty members, and 62 administrative staff members (N = 2121). Three role-adapted 75-item questionnaires covered the frequency and contexts of AI use, perceived usefulness, trust and control, academic integrity concerns, responsible-use norms, institutional policy clarity, and perceived improvement in output quality. Data analysis included descriptive statistics, Welch group comparisons, pooled ordinary least squares (OLS) models, reliability and dimensionality checks for observed indices, and exploratory student-only K-means clustering. The results revealed a pronounced AI adaptation gap across university groups. Students reported higher current AI-use intensity and perceived usefulness than faculty and administrative staff, whereas faculty and administrative staff reported stronger academic integrity concerns and greater endorsement of responsible-use norms. In the pooled OLS trust model, perceived usefulness had the strongest standardized positive association with trust in AI (β = 0.402); institutional policy clarity also had a positive but weaker association (β = 0.223). Students reported higher perceived policy clarity than faculty, while neither group differed significantly from administrative staff. Exploratory clustering indicated heterogeneity among students in experience, competence, usefulness, trust, and control, but did not establish a latent typology across university groups. The cross-sectional, self-reported data show associations and group differences rather than causal effects on learning or objective outcomes.
This academic research paper provides a comprehensive review of the concepts, stages, methodologies, tools, and real-world applications of data analysis. Special emphasis is placed on the transformative integration of Artificial Intelligence (AI) and Machine Learning (ML) across vital sectors such as education, healthcare, business, and finance. Furthermore, the study addresses critical challenges including data quality, privacy, security, and algorithmic bias, while featuring practical visual analytics and a case study on predictive modeling for student academic performance.
Paper Accepetd for publication in: Transactions on Artificial Intelligence ISSN: 2982-3439 (Scilight Press) Abstract Researchers increasingly rely on Clinical Artificial Intelligence (CAI) to derive predictive insights from massive observational datasets. However, the paradigm of Big Medical Inference often conflates statistical volume with clinical representativeness. This study argues that when methodological rigor is sacrificed for data scale, AI models risk institutionalizing historical biases, thereby compromising patient safety and public health policy integrity. We propose a precautionary, multi-level auditing framework designed to assess the structural architectural integrity of large-scale clinical datasets prior to computational deployment. Rather than contesting specific clinical outcomes, our approach establishes a quantitative prerequisite for CAI: the validation of demographic representativeness and baseline case distribution against official national benchmarks. To demonstrate the validity of our approach, we applied our framework to a prominent, high-profile case study. Our check revealed a tripartite structural divergence: a 32.5% demographic deficit in high-risk elderly (aged 65 years) strata, a 26.2% aggregate cancer incidence suppression, and a 45.1% deflation of expected cases in non-exposed groups.These discrepancies demonstrate that even massive datasets can be fundamentally misaligned with clinical reality. We conclude that structural validation is not an elective procedure but an ethical imperative for Human-Centric Clinical AI. By re-establishing this hierarchy of validation, we ensure that the intelligence of automated AI systems remains subordinate to the structural truth of the data, thereby transitioning from a reliance on Big Data alone toward a more robust, ethically sound, and authentic practice of clinical knowledge.
Marco Roccetti· Zenodo (CERN European Organi...· 0 citations
Abstract Background: Artificial intelligence (AI) holds promise in reshaping healthcare by transforming educational patterns, patient care, and research opportunities. However, there are obstacles impeding the proper integration of AI into the medical field. This study was undertakento evaluate the knowledge, attitude, and awareness of medical students and resident doctors regarding AI in medicine and healthcare. Methods:A questionnaire-based survey was conducted that included a total of 16 questions specifically designed to assess the knowledge and attitude of participants towards AI. The questionnaire used in the present study was developed for this study only and content validity of the initial questionnaire was adequately assessed. The questionnaire was converted into a Google Form, and participants were provided with the link to complete it. Statistical analysis was conducted using R version 4.3.2 (R-Studio). Results: Out of 194 respondents, 113 (58.25%) were medical students, and 81 (41.75%) were resident postgraduate doctors aged 19 to 32 (average 23.91 years) and a male-to-female ratio of 3.62:1. While 63.41% rated their AI knowledge as poor to below average, with 55.15% lacking understanding of many AI terminologies, 59.28% believed AI tools could enhance their understanding of medical concepts. 83.5% expressed interest in furthering knowledge on AI in healthcare. ChatGPT was the most used AI tool, primarily for language correction (50%), literature reviews and manuscript writing (43.3%), and creating presentation outlines (37.11%). Additionally, knowledge about AI devices and apps applicable to diagnostics, therapeutics, patient care, and data analysis was evaluated, along with opinions on barriers to incorporating AI in healthcare. 81.44% of respondents were unaware of AI's ethical considerations. Conclusions: AI has immense potential across diverse healthcare sectors. Nonetheless, our study also underscores the pressing need to confront challenges and equip our future healthcare professionals with the evolving realm of AI. This is essential to ensure they can effectively apply practical AI knowledge for enhanced patient care and management.
Ishan Gupta, Ankush Garg, Ashwin Varadarajan et al.· BMC Medical Education· 0 citations
AB156 CPF-Camouflage Police Force (Enhanced) A Preventive, Rehabilitative and Adaptive Policing Framework for Safer Cities and Communities Original Idea: 01 December 2019Author: Muhammad Asim – Global Progress VolunteerIndependent Researcher ID / ORCID: 0000-0002-8575-4447Relevant United Nations Sustainable Development Goal: SDG 11 – Sustainable Cities and Communities Abstract Street crime and urban insecurity remain important challenges for cities and communities worldwide. Although crime patterns differ considerably between countries and cities, effective public safety requires approaches that combine prevention, lawful enforcement, rehabilitation, community participation and evidence-based policing. This paper introduces the Camouflage Police Force (CPF), an original policing concept first developed by the author on 01 December 2019. CPF proposes an integrated three-stage framework: (1) root-cause prevention, (2) lawful surrender, rehabilitation and reintegration, and (3) adaptive camouflage policing. The distinctive element of CPF is its proposal to complement conventional visible policing with strategically less-obvious police deployment. As an initial research hypothesis, CPF proposes testing a 70% adaptive/non-obvious and 30% visible deployment model. This ratio is not presented as an established universal standard; rather, it should be experimentally evaluated and adjusted according to local crime patterns, legal requirements, operational capacity, community expectations and empirical results. The framework also proposes responsible use of artificial intelligence and data-driven tools for crime-pattern analysis and resource allocation, while emphasizing human oversight, privacy, due process, proportionality and independent accountability. CPF is aligned with the broader objective of United Nations Sustainable Development Goal 11, which calls for cities and human settlements to become inclusive, safe, resilient and sustainable [1]. It also reflects established evidence supporting multisectoral violence prevention, attention to risk factors, social reintegration and participatory urban safety strategies [2–6]. The paper concludes that CPF represents a researchable alternative to predominantly reactive policing by integrating prevention, rehabilitation and adaptive protection into a single framework. Keywords: Camouflage Police Force, CPF, street crime, urban safety, crime prevention, adaptive policing, hidden policing, rehabilitation, reintegration, artificial intelligence, SDG 11, sustainable cities, public safety.
Muhammad Asim - Global Progress Volunteer Muhammad Asim - Global Progress Volunteer· Zenodo (CERN European Organi...· 0 citations
Purpose This study examines factors associated with older adults' intention to use artificial intelligence (AI) for personal financial management in China and Vietnam by integrating the technology acceptance model (TAM) and the knowledge–behavior gap (KBG) model. Design/methodology/approach Using cross-sectional survey data from 713 respondents, including 407 respondents from Vietnam and 306 from China, the proposed model was examined using partial least squares structural equation modeling (PLS-SEM). Findings Information diagnosticity was positively associated with assessment perceived utility in Vietnam, whereas the corresponding association was not statistically significant in China. Social influence was positively associated with intention to use AI for personal financial management in China but showed no statistically significant association with intention in Vietnam. AI self-efficacy and AI literacy showed significant positive associations with several technology-evaluation and acceptance constructs in both countries. Neither personal innovativeness nor brand reputation significantly moderated the association between acceptance and intention in either national sample. Originality/value Research on AI adoption has predominantly focused on technologically experienced or younger populations. This study extends the literature by examining AI-supported personal financial management among middle-aged and older adults and by comparing the structural associations observed in two Asian countries with different technological and institutional environments.
Hà Vy Nguyễn, Ngoc Kim Ngan Le, Thi Hoang Trang Nguyen et al.· China Finance Review Interna...· 0 citations
AB156 CPF-Camouflage Police Force (Enhanced) A Preventive, Rehabilitative and Adaptive Policing Framework for Safer Cities and Communities Original Idea: 01 December 2019Author: Muhammad Asim – Global Progress VolunteerIndependent Researcher ID / ORCID: 0000-0002-8575-4447Relevant United Nations Sustainable Development Goal: SDG 11 – Sustainable Cities and Communities Abstract Street crime and urban insecurity remain important challenges for cities and communities worldwide. Although crime patterns differ considerably between countries and cities, effective public safety requires approaches that combine prevention, lawful enforcement, rehabilitation, community participation and evidence-based policing. This paper introduces the Camouflage Police Force (CPF), an original policing concept first developed by the author on 01 December 2019. CPF proposes an integrated three-stage framework: (1) root-cause prevention, (2) lawful surrender, rehabilitation and reintegration, and (3) adaptive camouflage policing. The distinctive element of CPF is its proposal to complement conventional visible policing with strategically less-obvious police deployment. As an initial research hypothesis, CPF proposes testing a 70% adaptive/non-obvious and 30% visible deployment model. This ratio is not presented as an established universal standard; rather, it should be experimentally evaluated and adjusted according to local crime patterns, legal requirements, operational capacity, community expectations and empirical results. The framework also proposes responsible use of artificial intelligence and data-driven tools for crime-pattern analysis and resource allocation, while emphasizing human oversight, privacy, due process, proportionality and independent accountability. CPF is aligned with the broader objective of United Nations Sustainable Development Goal 11, which calls for cities and human settlements to become inclusive, safe, resilient and sustainable [1]. It also reflects established evidence supporting multisectoral violence prevention, attention to risk factors, social reintegration and participatory urban safety strategies [2–6]. The paper concludes that CPF represents a researchable alternative to predominantly reactive policing by integrating prevention, rehabilitation and adaptive protection into a single framework. Keywords: Camouflage Police Force, CPF, street crime, urban safety, crime prevention, adaptive policing, hidden policing, rehabilitation, reintegration, artificial intelligence, SDG 11, sustainable cities, public safety.
Muhammad Asim - Global Progress Volunteer Muhammad Asim - Global Progress Volunteer· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Explainability and Interpretability of Black-Box Models in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of explainable AI and interpretability as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
