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        <title>Frontiers in Artificial Intelligence | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/artificial-intelligence</link>
        <description>RSS Feed for Frontiers in Artificial Intelligence | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-10-17T05:07:43.634+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1943572</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1943572</link>
        <title><![CDATA[A global machine learning framework for classifying national CO2 emission levels: responsible and explainable artificial intelligence for sustainable climate governance]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Muhammad Ali Khawaja</author><author>Muhammad Farooq Shaikh</author>
        <description><![CDATA[We investigate whether trade openness, governance, environmental taxation and selected macroeconomic indicators can classify the contemporaneous CO2 emission level of countries observed over 2000 to 2023, using data from the World Development Indicators and the OECD. CO2 emissions per capita are divided into three ordinal levels, low, medium and high, using tertile cut points estimated on the training data only within every validation split. Four classifiers, Random Forest, XGBoost, a support vector machine and a multinomial logistic regression, are tuned and compared alongside two models designed specifically for ordinal outcomes, an ordered logistic regression and an ordinal decomposition of XGBoost, under four validation designs: a country grouped holdout and country grouped 5 fold cross validation, in which no country appears in both the training and test sets; a chronological holdout; a 1 year ahead forecasting task; and a random stratified country year split, retained only as a reference point. Under country grouped cross validation, no classifier achieves a decisively higher accuracy than the others: nested cross validation gives a mean accuracy of 0.644 for XGBoost, 0.648 for Random Forest, 0.671 for the support vector machine, 0.674 for the multinomial logit, 0.676 for the ordered logit and 0.637 for the ordinal XGBoost model, with overlapping confidence intervals throughout. This is markedly lower than the 0.91 accuracy obtained under a random country year split with the same tuned pipeline, a gap of 12.5 percentage points for XGBoost, confirming that a substantial part of the high accuracy obtainable under random splitting reflects the model recognizing previously seen country profiles rather than generalizing to unseen countries. Accuracy falls less sharply under the chronological holdout and the 1 year ahead forecasting task, in the region of 0.65 to 0.74, indicating that the classifiers generalize better across time within a country than across countries. Permutation importance, SHAP and LIME consistently identify government effectiveness, urban population growth and environmental tax revenue as the features most strongly associated with model predictions, with a moderate contribution from trade openness and the smallest from GDP growth, a ranking that is stable across importance methods, learning algorithms and cross validation folds. These variables are predictors of the model's output, not estimated causal determinants of emissions, and the results are best read as evidence for climate risk monitoring and further policy evaluation rather than as a causal policy assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1887024</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1887024</link>
        <title><![CDATA[A machine learning framework for stage-structured predator–prey systems with fear effect: Sparse Identification, Gaussian Process Regression, and bifurcation analysis]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>G. Ramraj</author><author>T. Poornima</author>
        <description><![CDATA[This paper presents a comprehensive machine learning framework for the analysis of a stage-structured predator–prey system incorporating the fear effect, intra-specific competition among juvenile predators, and maturation delay. The three-species model, comprising prey (N), juvenile predators (P1), and mature predators (P2), is governed by a non-linear ordinary differential equation system in which the prey growth rate is modulated by a fear factor 1/(1+kP2). We first establish the existence and local stability of the coexistence equilibrium E* = (16.02, 16.09, 32.19) and rigorously identify a supercritical Hopf bifurcation at a critical fear intensity kcrit ≈ 0.067, beyond which the equilibrium becomes unstable and a stable limit cycle emerges. Five machine learning methods are deployed and benchmarked: (i) Sparse Identification of Non-linear Dynamics (SINDy) with a generic polynomial library, which recovers the predator equations exactly (R2 = 1.0) and approximates the prey equation (R2 = 0.9997); (ii) Dynamic Mode Decomposition (DMD), whose linear assumption yields poor reconstruction (R2 = −2.885 for P2), demonstrating the inadequacy of linear methods for this non-linear system; (iii) Gaussian Process Regression (GPR) with a periodic–RBF composite kernel, achieving RMSE values of 1.92, 1.03, and 0.84 from sparse noisy observations; (iv) a physics-constrained function approximation for recovering population trajectories from incomplete data; and (v) a polynomial surrogate ODE model for derivative field approximation. Additionally, the effect of maturation delay τ on system stability is examined through delay differential equation analysis with a fourth-order Runge–Kutta integrator, revealing delay-induced destabilization and transitions to limit cycle dynamics at τ ≥ 10. All numerical results are independently verified and all performance metrics are computed from actual analyses. The framework provides a rigorous, reproducible methodology for applying modern data-driven methods to stage-structured ecological models.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1928521</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1928521</link>
        <title><![CDATA[Artificial intelligence readiness in fragile states: a systematic review and theory-informed framework for workforce transformation in Somalia]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Ayan Mohamed Jama</author><author>Nashad Farah Mohamud</author><author>Mohamud Osman Hamud</author><author>Abdullahi Mohamud Hassan</author><author>Abukar Sheikh Mohamed</author>
        <description><![CDATA[BackgroundArtificial intelligence (AI) is increasingly recognized as a determinant of national productivity and competitiveness, yet the conditions required for its beneficial adoption, infrastructure, institutions, and human capital, remain unevenly distributed across the globe. Somalia, a fragile and conflict-affected state undergoing a prolonged process of institutional reconstruction, exemplifies this unevenness: a young, digitally engaged population and a strikingly dynamic private telecommunications and mobile-money sector coexist with weak public institutions, fragmented higher education, and chronic graduate unemployment.MethodsThis paper reports a systematic literature review with theory-informed framework development, conducted in accordance with the PRISMA 2020 reporting guideline, that synthesizes peer-reviewed evidence on organizational and national AI readiness, digital-skills development, and workforce productivity, with a specific focus on Somalia and comparable fragile, low-connectivity, Sub-Saharan African contexts. Using the Technology–Organization–Environment (TOE) framework and human capital theory as complementary lenses, the review synthesizes findings from 31 sources identified through a structured search and screening process.ResultsThe evidence indicates that Somalia’s AI readiness gap is not primarily a technological deficit but a multidimensional one, spanning fragmented digital infrastructure, weak institutional governance and data ecosystems, a persistent mismatch between higher-education curricula and labour-market needs, and the absence of coordinated national AI or digital-skills policy.ConclusionBuilding on these findings, the paper proposes a four-pillar conceptual AI readiness framework intended to guide future empirical assessment and policy discussion, encompassing digital infrastructure and access, institutional and governance readiness, human capital and skills development, and labour-market integration, together with a phased implementation roadmap for government, higher-education institutions, the private telecommunications sector, and development partners. The review provides a literature-grounded, context-specific synthesis of AI readiness for a fragile Horn of Africa state and offers a transferable analytical approach for similar settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1903150</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1903150</link>
        <title><![CDATA[A recall-optimized LSTM–XGBoost ensemble model for river flood forecasting using multivariate time-series data—case study in Sri Lanka]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>T. H. S. Sankalpa</author><author>G. H. C. Sandaruwan</author><author>A. M. Aslam Saja</author><author>N. T. M. Sajith</author><author>M. F. M. Ishqy</author>
        <description><![CDATA[Riverine (fluvial) flooding remains a significant climate-related disaster in many countries. Extreme rainfall events caused by climate change cause significant damage and disruption to communities living near riverbanks or neighboring land areas, particularly when risk-sensitive development is ignored. This necessitates an effective mechanism to predict river flooding events and provide timely early warnings to the most vulnerable populations in high-risk flood-prone areas. The dynamics of flood risks require a localized, automated, and scalable solution that can be centrally monitored. This study presents a river flood forecasting system integrating artificial intelligence (AI) and Internet of Things (IoT). This system was deployed in one of the flood-prone river basins in Sri Lanka to assess the operational feasibility of real-time hydrological monitoring and flood-risk classification. The proposed system employed two machine learning models, Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost), for flood-risk prediction and IoT sensors (rainfall and ultrasonic sensors) for real-time hydrological data collection. The machine learning models were developed using rainfall, discharge rate, and river water level data to predict flood-risk states. A rule-based ensemble strategy was implemented by combining the outputs of LSTM and XGBoost to prioritize positive-class detection and reduce missed positive-labeled observations. Model performance was evaluated primarily using recall for the positive flood cases, while the overall accuracy was reported as the complementary metric alongside positive-class recall, providing an overall assessment of classification performance. Among the evaluated input window configurations using the same chronological holdout, the rule-based ensemble model with a 1-day input window showed the most favorable observed balance of performance metrics, achieving 100% positive-class recall, with 98.73% accuracy, 50.00% positive-class precision, and 66.67% positive-class F1-score. The 100.00% recall was based on correctly classifying all four positive-labeled observations in the held-out test set (4/4; 95% exact binomial CI: 39.8%−100.0%) and these observations occurred consecutively from November 25 to 28, 2024, and represent one continuous positive-labeled flood episode. The proposed safety-oriented AI-IoT framework demonstrates the operational feasibility of integrating real-time sensing with AI-based flood forecasting; however, longer-term field deployment, including actual flood events, is required to evaluate flood-event prediction performance under real-world flood conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1884749</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1884749</link>
        <title><![CDATA[Conditional diffusion-based inverse kinematics for redundant 7-DOF manipulators with differentiable refinement]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lesia Hentosh</author><author>Yaroslav Hladun</author><author>Ivan Tsmots</author>
        <description><![CDATA[BackgroundInverse kinematics of redundant manipulators is a multi-solution problem, since one end-effector pose may correspond to multiple admissible joint configurations. Classical numerical solvers and regression-based neural models usually return a single joint configuration and therefore do not explicitly represent the inverse-kinematic solution manifold. Existing generative solvers based on normalizing flows produce multiple solutions, but they require invertible network architectures and do not include an explicit task-space correction stage. This study aimed to develop a hybrid generative-kinematic method for inverse kinematics of a redundant 7-DOF manipulator that can generate multiple admissible configurations for a given target pose.MethodsA hybrid method, DiffusionIK, was developed by combining conditional diffusion-based generation, classifier-free guidance, DDIM sampling, and differentiable forward-kinematics refinement. A ResMLP denoiser is trained in the normalized joint space using procedurally generated training pairs obtained from the forward kinematics of the xArm manipulator. For each target pose, the diffusion model generated multiple candidate joint configurations, and the refinement stage subsequently reduced the task-space residual for each candidate. Numerical experiments were performed on 500 reachable target poses with 50 generated candidates per pose. Performance was evaluated using position and orientation success thresholds of 1 mm and 1°, respectively, together with the preservation of joint-space variability after refinement.ResultsDiffusionIK reached 99.40% success after 200 refinement steps and 100.00% after 500 steps under 1 mm and 1° thresholds, with a mean joint-space variability of 9.77 rad. In a unified benchmark against damped least squares, KDL, direct regression, and a normalizing-flow baseline, DiffusionIK with refinement achieved 99.80% success on 500 unseen poses and 98.40% near singularities, matching the strongest numerical solver while returning 50 diverse solutions per pose.ConclusionThe proposed DiffusionIK method provides an effective formulation of redundant inverse kinematics as conditional generation followed by local kinematic correction. By combining diffusion-based candidate generation with differentiable forward-kinematics refinement, the method represents the multi-solution nature of redundant manipulators and achieves high task-space accuracy while preserving configuration diversity. Unlike normalizing-flow-based solvers, DiffusionIK imposes no invertibility constraints on the denoiser and combines generation with differentiable forward-kinematics refinement, which provides sub-millimetre and sub-degree accuracy together with configuration diversity.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1954571</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1954571</link>
        <title><![CDATA[How gender shapes public speaking in virtual reality: a multimodal analysis]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Marion Ristorcelli</author><author>Rémy Casanova</author><author>Magalie Ochs</author>
        <description><![CDATA[Virtual Reality (VR) has proven effective for public speaking training, particularly due to its ability to simulate immersive speaking environments and automatically extract behavioral cues reflecting performance. However, limited attention has been paid to the impact of speaker gender and the gendered appearance of the virtual audience on the public speaking experience in VR. To address this gap, we conducted an experiment with 55 participants who performed oral presentations within a VR simulation that allowed for the systematic manipulation of the virtual audience's gendered appearance and social attitude. We captured a comprehensive dataset of multimodal nonverbal behavioral data, alongside subjective evaluations from both the participants and an expert using standardized and non-standardized questionnaires. By combining statistical analysis and machine learning approaches, our findings revealed no significant effect of speaker gender on her/his emotional experience or her/his public speaking performance. In contrast, male and female speakers exhibited distinct nonverbal behavioral patterns, identified through both expert evaluation and automatic multimodal behavioral analysis. Moreover, Virtual audience social attitude influenced participants' emotional experience and self-reported performance, whereas no significant effect of audience gendered appearance was detected. These findings highlight the importance of considering behavioral variability when studying and assessing public speaking in VR.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1909448</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1909448</link>
        <title><![CDATA[Prospective validation of multimodal risk stratification using clinical and quantitative imaging features for 12-month outcomes after minimally invasive surgery for lumbar disc herniation]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jichong Zhu</author><author>Hao Li</author><author>Chunxian Lu</author><author>Hengkua Huang</author><author>Tianyou Chen</author><author>Sitan Feng</author><author>Jiarui Chen</author><author>Ruifei Bai</author><author>Xinli Zhan</author><author>Chong Liu</author><author>Chengqian Huang</author>
        <description><![CDATA[BackgroundLumbar disc herniation (LDH) is a common spinal disorder leading to chronic low back pain and radiating leg pain. Minimally invasive surgery is the preferred treatment, but postoperative recovery varies significantly, making accurate preoperative prognosis prediction a challenge.MethodsThis study developed and prospectively validated a multimodal risk-stratification framework integrating structured clinical variables, conventional imaging parameters, CT/MRI radiomics, and high-dimensional image-derived features. A total of 773 patients were analyzed, including 665 patients in the retrospective development cohort and 108 patients in an independent prospective validation cohort. The retrospective cohort was divided into a training cohort (n = 532) and an internal validation cohort (n = 133). Feature selection, parameter optimization, and classification-threshold determination were performed exclusively within the training cohort, and the finalized framework was subsequently evaluated in the internal and prospective validation cohorts.ResultsThe integrated multimodal approach achieved an ROC-AUC of 0.937 (95% CI, 0.900–0.975) and a PR-AUC of 0.849 in the internal validation cohort, and an ROC-AUC of 0.976 (95% CI, 0.951–1.000) and a PR-AUC of 0.941 in the prospective validation cohort. Its discrimination was comparable to that achieved using structured clinical and conventional imaging information alone and was higher than that of isolated quantitative imaging approaches. At the prespecified training-derived classification threshold of 0.318, sensitivity, specificity, and balanced accuracy in the prospective cohort were 85.3%, 95.9%, and 90.6%, respectively. Calibration analysis demonstrated residual probability miscalibration despite strong discrimination, although training-derived logistic recalibration improved the Brier score.ConclusionMultimodal integration of clinical and quantitative imaging information demonstrated strong discrimination for 12-month postoperative outcome stratification in both internal and prospective validation cohorts. However, the incremental discriminative value beyond structured clinical and conventional imaging information was limited, and residual miscalibration remained in the prospective cohort. Further multicenter validation, standardized quantitative imaging workflows, and calibration assessment are warranted before clinical translation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1948601</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1948601</link>
        <title><![CDATA[A hybrid CNN–BiLSTM network with additive attention for automated four-stage classification of Alzheimer's disease from brain MRI]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>T. Deenadayalan</author><author>S. P. Shantharajah</author><author>Swarna Priya Ramu</author>
        <description><![CDATA[AD is a progressive and irreversible neurodegenerative disorder. Because the structural changes that accompany the earliest stages of the disease are subtle and easily overlooked, manual interpretation of MRI is time-consuming and prone to inter-observer disagreement. This study presents an end-to-end deep learning framework that fuses spatial and sequential reasoning to classify brain MRI into four clinically meaningful stages: Person with no dementia, Person with very mild dementia, Person with mild dementia, and Person with moderate dementia. An additive attention mechanism subsequently weights the most informative positions before a compact dense head produces the final decision. Class imbalance is addressed by capped oversampling of real minority slices combined with residual cost-sensitive weighting, applied to the training fold only; pixel-space SMOTE is deliberately avoided because interpolating between two unregistered MRI slices produces images without an anatomical referent. Exact-duplicate screening is applied prior to partitioning, and a quantitative near-duplicate audit is reported for the fixed folds. Trained on 6,400 grayscale MRI slices under a stratified 70/15/15 protocol, the proposed CNN–BiLSTM–Attention model attains a test accuracy of 95.94%, a balanced accuracy of 96.08%, an MCC of 0.9334, a Cohen's kappa of 0.9325 and a macro-averaged one-vs.-rest AUC of 0.9954, on a slice-level held-out test fold. Per-class analysis shows strong sensitivity and specificity for the three better-represented stages; the extremely rare Person with moderate dementia class (nine test images) shows a favorable point estimate but is too small to support a claim of robust detection on its own. Attention maps and Grad-CAM overlays localize the evidence the network uses, and an error-structure analysis shows that no misclassification spans more than two stages. Because the corpus carries no subject identifiers, patient-grouped partitioning is not possible and the reported accuracies should be interpreted as an upper bound attainable under a slice-level split; this constraint is quantified explicitly and motivates the external-validation study outlined in the manuscript. Overall, the results highlight the methodological value of coupling convolutional feature extraction with bidirectional sequence modeling and additive attention under a leakage-aware, imbalance-aware evaluation protocol for multi-stage AD staging.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1800219</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1800219</link>
        <title><![CDATA[Encoding tactile stimuli for Braille recognition with organoids]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tianyi Liu</author><author>Hemma Philamore</author><author>Benjamin Ward-Cherrier</author>
        <description><![CDATA[This study proposes a transferable encoding strategy that maps tactile sensor data to electrical stimulation patterns, enabling neural organoids to perform an open-loop artificial tactile Braille classification task. Human forebrain organoids cultured on a low-density microelectrode array (MEA) are systematically stimulated to characterize the relationship between electrical stimulation parameters (number of pulse, phase amplitude, phase duration, and trigger delay) and organoid responses, measured as spike activity and spatial displacement of the center of activity. Implemented on event-based tactile inputs recorded from the Evetac sensor, our system achieved an average Braille letter classification accuracy of 61% with a single organoid, which increased to 83% when responses from a three-organoid ensemble were combined. Additionally, the multi-organoid configuration demonstrated enhanced robustness against various types of artificially introduced noise. This research demonstrates the potential of organoids as low-power, adaptive bio-hybrid computational elements and provides a foundational encoding framework for future scalable bio-hybrid computing architectures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1985517</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1985517</link>
        <title><![CDATA[Interpretable detection-based image classification for differential diagnosis of vaginal discharge using smartphone-assisted microscopy]]></title>
        <pubdate>2026-10-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Siriwoot Sookkhee</author><author>Marut Wongtapin</author><author>Trin Phuriyaphan</author><author>Kornkaran Tonpiriyaphong</author><author>Nitan Wachirapaet</author><author>Surapee Wasanapitanont</author><author>Jidapa Joysa</author><author>Monsicha Pongpom</author><author>Narin Lawan</author><author>Phit Upaphong</author><author>Tanaporn Wangsanut</author>
        <description><![CDATA[BackgroundThe vaginal discharge is a common clinical presentation associated with diverse infectious and microbiota-related conditions. Existing artificial intelligence (AI)-based diagnostic approaches have predominantly focused on detection of individual pathogens and have rarely integrated microbiota-associated conditions within a diagnostic framework. We developed an explainable deep learning framework for automated analysis of Gram-stained vaginal smear images acquired using handheld smartphones or tablets positioned over a conventional light microscope eyepiece.MethodsThe framework integrates object detection with image-level classification, termed detection-based image classification, to identify diagnostically relevant microscopic structures without requiring exhaustive enumeration of all objects. Five diagnostic categories included vulvovaginal candidiasis (VVC), bacterial vaginosis (BV), trichomoniasis (TV), gonococcal urethritis/cervicitis (GU), and healthy vaginal condition (HL). YOLOv26, RT-DETR and EfficientDet0 were independently trained and evaluated using 1,966 microscopic images comprising 11,069 annotated diagnostic objects. Model explainability and computational benchmarking were assessed. The localization quality was independently evaluated by eight clinicians and microbiologists.ResultsClass-specific analysis demonstrated that TV and GU consistently achieved the highest detection performance across all models (mAP50 = 0.622–0.752), whereas RT-DETR demonstrated superior performance for VVC, BV, and HL. Expert evaluation showed favorable localization, with Good/Excellent ratings in 84.8 and 85.1% of valid evaluations for YOLOv26 and RT-DETR, respectively, with no significant difference between models (OR = 0.97; 95% CI, 0.58–1.61; p = 0.895).ConclusionThis study presents an accurate and clinically interpretable framework that integrates microbiota-associated conditions and sexually transmitted infections for differential diagnosis of vaginal discharge using conventional light microscopy. By linking image-level predictions to localized diagnostic morphology, the framework provides human-verifiable AI evidence as a proof-of-concept prototype. While promising for low-cost implementation, rigorous prospective multi-center validation is required before broad clinical deployment or autonomous diagnosis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1985132</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1985132</link>
        <title><![CDATA[Measuring a scaffold from inside: an operator-authored self-measurement harness for a constitutional runtime over a hosted language model]]></title>
        <pubdate>2026-10-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Richard Dobson</author>
        <description><![CDATA[Interactive intelligent systems constructed over hosted large language models cannot retrain the substrate on which they depend, and so their designers enforce a named epistemology at the surrounding scaffold layer. A difficulty follows that has received little methodological attention: when quality regresses, the regression frequently belongs to the scaffold rather than the substrate, and identifying it requires instruments that measure the scaffold itself rather than the model beneath it. This article reports one such instrument set, added to a live constitutional runtime between 17 and 18 July 2026 and extended with a production drift observer on 19 August 2026, and situates it as a reusable design pattern. The layer comprises six components—operator-authored adversarial batteries held as versioned regression fixtures, deterministic pattern screens encoding named defects, an emission quarantine that retains failure evidence as a first-class object, an instrument-health monitor issuing autonomous handoffs, continuous-integration delivery tripwires, and an observe-only production drift detector that accumulates a hand-labelling corpus from live traffic without altering any replies. Eight falsifiable harness predictions are stated (H1 through H8), each named against a specific runtime carrier—a code path, a database table, or a typed field—and each paired with an explicit falsification condition; their relationship to the eight pre-registered outcome predictions (F1 through F8) of a companion architecture paper is stated in a dedicated reconciliation section, since the harness reported here is the instrument set on which those outcome predictions depend. Of the eight, five (H1, H2, H3, H5, H7) carry dated on-platform observations and are reported in a dedicated observed subsection; three (H4, H6, H8) are prospective—predictions about experiments that have not yet occurred—and are reported separately under that label, so that no reader mistakes the distribution of evidence. The predictions are offered for adversarial replication under an honest empirical frame rather than as settled results: the batteries were authored by the platform’s operator, classification is not independent, and the number of platforms studied is one. The discussion states a narrow priority claim, distinguishes the enforcement point of composition-time assertion from adjacent techniques, extends the pattern to the governance of learning loops—a scaffold that adapts must be measured by the same discipline that measures a scaffold that enforces—and argues that the interaction properties a reader encounters, an observation lock and per-turn provenance grounding, become testable precisely because they are structurally enforced.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1906883</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1906883</link>
        <title><![CDATA[A multilingual heterogeneous knowledge graph framework for curriculum–vacancy–MOOC alignment: edge-weight regression with heterogeneous graph transformers for educational recommendation]]></title>
        <pubdate>2026-10-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sandugash Serikbayeva</author><author>Madina Sambetbayeva</author><author>Valiya Ramazanova</author><author>Zhanar Lamasheva</author><author>Aigerim Yerimbetova</author><author>Valentina Makhatova</author><author>Zhanna Sadirmekova</author>
        <description><![CDATA[IntroductionDigitalisation and artificial intelligence have widened the gap between competencies certified by university curricula and skills demanded by employers. MOOCs could close this gap, but existing recommenders rarely connect what a programme teaches, what the market requires, and what online courses develop within a single semantic structure.MethodsWe built a multilingual heterogeneous knowledge graph (KG) that integrates university programmes, IT vacancies from hh.kz, and Coursera MOOCs in English, Russian, and Kazakh. A property-graph ontology was formalised and instantiated automatically. Skills were aligned with a fine-tuned multilingual Sentence Transformer (paraphrase-multilingual-mpnet-base-v2), and agglomerative clustering formed skill communities. Recommendation was cast as edge-weight regression over a competency-to-course relation and solved with GraphSAGE, HAN, and HGT encoders.ResultsOn a held-out 20% test graph, HGT achieved the best ranking quality (NDCG@10 = 0.714; with short meta-paths NDCG@10 = 0.987, RMSE = 0.108), outperforming GraphSAGE and HAN. Applied to the “Information Systems” programme of a Kazakhstani university, the system expanded the market-aligned competency profile by +36.2% with five top-ranked courses and by +170% with the full catalogue.DiscussionEdge-weight regression on a cross-domain heterogeneous KG produces front-loaded, actionable course recommendations for curriculum alignment. All datasets, models, graph snapshots, and code are released for reproducibility.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1944175</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1944175</link>
        <title><![CDATA[PharmAI: domain-specific QA generation by adaptive learning and fine-tuning of small language models]]></title>
        <pubdate>2026-10-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shenbagarajan Anantharajan</author><author>Saranraj Loganathan</author><author>Rajkumaran Soundarraj</author><author>E Emerson Nithiyaraj</author>
        <description><![CDATA[BackgroundDomain-specific medical question-answering (QA) models face bottlenecks due to scarce labeled data, specialized terminology, and constrained computational hardware.ObjectivesThe study aims to develop an efficient pharmaceutical question-answering (QA) system that can be trained and deployed on consumer-grade hardware using Small Language Models (SLMs).Materials and methodsThe pipeline processes four pharmaceutical textbooks containing 3,977 total pages, producing 7,370 semantically similar text chunks and 2,144 synthetic QA pairs under four questioning categories: definition, mechanism, comparison, and clinical application. Three Small Language Models (SLMs), namely SmolLM2-1.7B-Instruct, Qwen2.5–1.5B-Instruct, and Llama-3.2–1B-Instruct, are fine-tuned under the same QLoRA (Quantized Low-Rank Adaptation) configurations and then evaluated with retrieval metrics (Recall@K, Mean Reciprocal Rank) and generation metrics (ROUGE-1/2/L, Semantic Similarity, BERTScore). Architectural decisions including NF4 4-bit quantization, CPU-based FAISS indexing, and completion-only loss masking are implemented due to hardware constraints and validated via ablation analysis.ResultsSmolLM2-1.7B-Instruct, Qwen2.5–1.5B-Instruct, and Llama-3.2–1B-Instruct demonstrated identical retrieval performance, achieving a Recall@1 of 0.517, Recall@3 of 0.732, Recall@5 of 0.797, and an MRR of 0.628. Similarly, the generation performance evaluation revealed that Qwen2.5–1.5B FT outperformed the other fine-tuned SLMs, achieving the highest scores across all metrics (ROUGE-1 = 0.47, ROUGE-2 = 0.25, ROUGE-L = 0.38, and BERTScore = 0.72). In comparison, SmolLM2-1.7B FT and Llama-3.2–1B FT exhibited comparable performance, with ROUGE-1 scores of approximately 0.35, ROUGE-2 scores of 0.16–0.17, ROUGE-L scores of 0.25–0.26, and BERTScores of 0.66, indicating lower lexical and semantic alignment with the reference outputs than Qwen2.5–1.5B FT.ConclusionWe evaluate retrieval and generation performance using comprehensive metric suites, incorporating a category-wise breakdown that reveals comparison questions to be the most challenging for single-passage retrieval architectures.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1931300</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1931300</link>
        <title><![CDATA[Landscape and training opportunities in artificial intelligence for biosciences in Latin America: the BiotrAIn project]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Jose Arturo Molina-Mora</author><author>Flavio Ezequiel Spetale</author><author>Tulio de Lima Campos</author><author>Pedro E. Romero</author><author>Lucas Inchausti</author><author>Adrian Gustavo Turjanski</author><author>Ignacio Alcántara</author><author>Maria Fernanda Ribeiro Dias</author><author>Carla V. Filippi</author><author>Matías Domínguez</author><author>Juan Bizzotto</author><author>Daniela Senra</author><author>J. Noé García-Chávez</author><author>Claudia Hoepfner</author><author>Cleidy Osorio-Mogollon</author><author>C. Paola Rangel-Chávez</author><author>Valeria Foronda</author><author>Alejandra Rougon-Cardoso</author><author>Nelly Selem-Mojica</author><author>Yesid Cuesta-Astroz</author><author>Cindy Aguilar-Bartels</author><author>Rebeca Campos-Sánchez</author><author>Lizzie Tchongwe Divala</author><author>Kim Tamara Gurwitz</author><author>Cath Brooksbank</author>
        <description><![CDATA[Artificial Intelligence (AI) is redefining biosciences by enabling the analysis of complex, large-scale biological and medical data. Latin America has demonstrated remarkable potential through studies applying AI to biomedical sciences often using locally generated data and addressing regional needs. However, the integration of AI into research and healthcare in this region faces several structural challenges, including insufficient infrastructure, lack of specialized training, fragmented research systems, and continuously constrained funding. To overcome existing limitations, capacity building at the regional level is crucial. This includes developing localized AI training programs and the reinforcement of a collaborative research framework. Collaboration across institutions and countries, along with engagement from industry and government, is key to fostering an enabling environment for the responsible and effective use of AI. One promising response to these challenges is the BiotrAIn project, a partnership involving EMBL-EBI, the University of Costa Rica, and CABANAnet, with support from the Chan Zuckerberg Initiative. The project is structured around three pillars: knowledge exchange, curriculum development, and regional training. BiotrAIn exemplifies how strategically designed, regionally adapted programs can build AI capacity in biosciences and foster more equitable scientific development across Latin America. Strategic efforts like BiotrAIn serve as essential building blocks toward a more robust, ethical, and sustainable AI ecosystem in the region, while offering a model that could be adapted by other regions globally. Ultimately, with coordinated investment, inclusive policies, and collaborative networks, Latin America can position itself as a significant contributor to global AI-driven bioscience.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1913975</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1913975</link>
        <title><![CDATA[Deep network-guided key map generation for lossless image encryption]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>C. Mahapriya</author><author>V. Thanikaiselvan</author><author>Shweta B. Thomas</author>
        <description><![CDATA[In this article, a novel 4D Non-monotonic Sinusoidal Chaotic Map (NSCM) for confusion and a deep network-guided key map generation for diffusion are introduced to improve information security against various attacks. This article utilizes the advantages of the sinusoidal function to modify the monotonic function for generating random sequences to obtain a chaotic map for permutation. The limitations of substitution, such as chosen/known plaintext attacks, are suppressed using deep network-based key map generation. A key map equal in size to the size of the plaintext image is obtained as a generative output from a deep network using a smaller map and normalized to the intensity range of the plaintext image. The plaintext image is scrambled block-wise, followed by pixel-wise confusion using the chaotic map. Then the scrambled image is diffused with the key map using a bit-level XOR operation to obtain the encrypted image. The proposed method can be reversed for retrieving the plaintext image, that is, diffusion followed by confusion. The experimental results show that the proposed method performs better qualitatively in terms of visual perception as well as quantitatively in terms of entropy and Peak Signal-to-Noise Ratio (PSNR). Also, the proposed method strongly restricts various attacks such as chosen-plaintext attacks and differential attacks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1930016</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1930016</link>
        <title><![CDATA[Hjort-YOLO: a lightweight and explainable computer vision framework for wildlife detection]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Md Abdul Momin</author><author>Mehak Khan</author><author>Reza Arghandeh</author><author>Richard Bischof</author><author>Stein Joar Hegland</author><author>Terje Moen</author><author>Martin F. Stølen</author>
        <description><![CDATA[IntroductionAutomated animal detection is an important component of ecological monitoring and may in the future be used also for wildlife vehicle collision mitigation by constructing efficient alert systems. However, the often complex environmental background in habitats along roads and different sizes due to the distance from the camera lens of animals in the images may reduce the detection accuracy, whereas high-precision detection requires computationally expensive object detection methods. This paper presents Hjort-YOLO, an efficient computer vision framework for detecting wildlife from camera-trap images acquired under challenging background conditions.MethodologyThe proposed framework was developed by enhancing the lightweight YOLOv11n architecture through targeted modifications to the backbone and detection head by improving the cross-stage partial convolutional block (C2f-W), an area attention block (A2C2f-W), and reducing a detection head compared to the YOLOv11n algorithm, enabling an optimal balance between detection accuracy and computational efficiency. Hjort-YOLO is evaluated on the custom Svanøy deer dataset and the African Wildlife dataset, which include complex forest scenes, occlusions, background clutter, and motion blur.ResultsExperimental findings indicate that the Hjort-YOLO achieves detection accuracy comparable to state-of-the-art object detection models while significantly improving runtime performance. Hjort-YOLO provides 0.2 percent, 13 percent, and 17 percent better mean average precision, frames per second, and inference time, respectively, while detecting wildlife compared to YOLOv11n. The computation complexity, such as Giga Floating Operations per Second, was reduced by 19 percent compared to the YOLOv11n algorithm.DiscussionThese performance gains show the suitability of the proposed framework for detecting wildlife from ground-based sensors like camera traps, providing real-time performance and high detection accuracy in resource-constrained platforms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1969257</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1969257</link>
        <title><![CDATA[AI use and risk disclosure by investment advisers]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Samir Chincholikar</author><author>Robin Chawla</author>
        <description><![CDATA[IntroductionInvestment advisers increasingly report using artificial intelligence (AI), but little is known about what they disclose about it in fiduciary filings. The SEC brought its first AI-washing enforcement actions in 2024, so regulators and researchers need a reproducible measure of AI disclosure. We provide large-sample evidence on AI disclosure in Form ADV Part 2A brochures and show how the estimate depends on measurement design.MethodsWe drew a stratified random sample of 400 SEC-registered investment advisers from a population of 16,223 firms, crossing adviser type (private-fund vs. wealth/retail) with 2024 assets-under-management (AUM) quartile. A large language model (LLM) classified the 388 available brochures under a prespecified five-label typology: investment-process use, operational or client-service use, AI-related risk, explicit non-use, and named AI vendors. We treated the LLM as a measurement instrument and validated it three ways: same-family reproducibility, blinded cross-family recoding of 180 brochures, and human verification of 158 cases. Secondary analyses compared brochures with 283 matched firm websites and applied a screen derived from SEC enforcement orders.ResultsAI use is disclosed in 23.7% of brochures (22.7% survey-weighted), while 27.6% contain AI-related risk language. The within-brochure difference is significant (McNemar p = 0.036). Disclosure rises from 10.4% in the smallest AUM quartile to 43.8% in the largest, and is higher among private-fund advisers. Against human coding, the composite any-use measure achieves precision of 0.76 and sensitivity of 0.81. The same corpus yields estimates of 9.9%, 13.1%, and 23.7% under alternative constructs, adviser universes, and weighting schemes. A literal keyword rule misses or mislabels roughly a quarter of cases. A raw 17.3-percentage-point brochure–website gap disappears after conditioning on document length. None of the five enforcement-screen matches was adjudicated as an unsubstantiated promotional claim.DiscussionAdvisers present AI at least as often as a risk as a capability, and disclosure is concentrated among larger and private-fund advisers. AI-disclosure prevalence reflects measurement design as much as adviser behavior, so estimates should be reported with their construct, universe, weighting, and validation. The validated framework and an open reproducibility capsule provide a baseline for monitoring AI claims and AI-washing in investment management.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1858302</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1858302</link>
        <title><![CDATA[AI-driven lithium-ion battery health prediction: a review of SOC, SOH and RUL estimation techniques]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Review</category>
        <author>G. Tamizharasi</author><author>G. K. Rajini</author>
        <description><![CDATA[The need for precise lithium-ion battery (LiB) monitoring and prognostics has grown significantly with the increasing use of electric vehicles (EVs) and renewable energy system (RES) technologies. State of charge (SOC), state of health (SOH), and remaining useful life (RUL) are three critical performance metrics for ensuring battery safety, functional dependability, and effective energy utilization. Non-linear degradation patterns, operating uncertainties, and dynamic environmental fluctuations are frequently difficult for traditional electrochemical and empirical assessment methods to capture. As a result, recent research has focused on advanced data-driven techniques for comprehensive battery state estimation, notably those based on artificial intelligence (AI) and deep learning (DL). This review provides an overview of current methodologies for SOC, SOH, and RUL prediction, with a focus on modern DL architectures such as transformer networks, autoencoder-based latent modeling, neural basis expansion techniques, attention mechanisms, and hybrid optimization-driven frameworks. The article examines new advances in intelligent battery prognostics critically, emphasizing their potential for improved estimation precision, resilience, and flexibility in practical operating scenarios. In addition, it identifies future research directions such as enhanced optimization-assisted frameworks, hybrid physics-informed learning, and explainable artificial intelligence (XAI). This article intends to offer a comprehensive overview of modern battery prognostics approaches to aid in the development of dependable, efficient, and long-term battery management system (BMS) solutions for electric mobility and energy storage systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1964400</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1964400</link>
        <title><![CDATA[Deep learning-based android malware detection under realistic evaluation constraints: an architecture-centric review and reliability-aware empirical validation]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Syed Shuja Hussain</author><author>Mohd Faizal Ab Razak</author><author>Idris Ismail</author><author>Ahmad Firdaus</author><author>Abdul Azeem</author><author>Jamil Zara</author><author>Shamim Akhter</author>
        <description><![CDATA[IntroductionDeep learning (DL) has reshaped Android malware detection (AMD) by enabling models to learn from heterogeneous Android artifacts, including permissions, DEX and bytecode representations, API and system-call traces, network traffic, image encodings, and program graphs. However, high benchmark accuracy does not necessarily imply reliable, robust, explainable, reproducible, or deployable malware detection.MethodsThis study examines DL-based AMD under realistic evaluation constraints through an architecture-centric systematic review and reliability-aware empirical validation. A PRISMA-guided review of studies published between 2021 and 2026 was conducted to analyze architecture families, artifact representations, dataset transparency, validation realism, robustness and evasion evidence, explainability, reproducibility, and deployment readiness. Controlled empirical validation was also conducted on CICMalDroid 2020 using representative CNN, GRU, Transformer, and Hybrid CNN–LSTM models.ResultsThe review shows that DL-based AMD is architecturally diverse, but its supporting evidence remains incomplete. Most studies report datasets, feature sources, model architectures, and conventional metrics, whereas robustness testing, artifact-grounded explainability, reproducibility assets, and deployment evidence remain inconsistent. The empirical validation shows that although all models achieve strong clean cross-validation performance, class-wise analysis, malware false-negative behavior, repeated-run stability, and multi-level test-time feature masking reveal architecture-dependent reliability differences that are not captured by aggregate accuracy.DiscussionThe study contributes an architecture–artifact–validation triad and a claim-aligned reliability audit to align AMD claims with the maturity of supporting evidence. Overall, the findings support a transition from accuracy-centric benchmarking to reliability-centric evaluation, where models are assessed using credible datasets, realistic validation, robustness, interpretability, reproducibility, and operational feasibility.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1973907</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1973907</link>
        <title><![CDATA[Generative AI in aging societies: the risk of a double divide]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>Alexander Seifert</author>
        <description></description>
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