Singapore

Malaria remains a major health challenge in Africa, with accurate species identification critical for prompt treatment and control, especially in resource-limited settings like Rwanda. This study systematically benchmarks state-of-the-art detection architectures for automated multi-species malaria parasite detection from high-resolution Giemsa-stained blood smear images, addressing the persistent problems of class imbalance and the difficulty of detecting small Plasmodium falciparum ring forms. We evaluate and optimize YOLO-SPAM, YOLO-Para, and YOLOv12 models, applying a novel species-specific augmentation protocol with copy-paste and noise injection. YOLOv12, trained with these protocols, achieves outstanding overall performance ( , and demonstrates significant improvement in detecting small, clinically relevant P. falciparum parasites over non-attention YOLO methods). Comparative experiments reveal that targeted data augmentation and strategic model selection can overcome significant class imbalance, achieving reliable multi-species differentiation and precise localization even for morphologically subtle classes. Our findings validate the practical promise of advanced object detection models, such as YOLOv12, for malaria diagnosis workflows and support their future deployment in real-world microscopy labs in Rwanda and similar endemic regions.

AAAI 2026

Bridging the Gap in Malaria Diagnostics: An Attention-Centric YOLO Framework with Species-Specific Augmentation for Tiny Parasite Detection in Low-Resource Settings

clinical deployment

rwanda

ring-stage parasite

copy-paste augmentation

blood smear

species-specific augmentation

multi-species classification

plasmodium falciparum

yolov12

malaria detection

evaluation and analysis

medical and biological imaging

object detection & categorization

medical imaging

segmentation

object detection

class imbalance

attention mechanism

deep learning

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Antimicrobial resistance (AMR) is becoming a bigger threat to global health, especially in low and middle-income countries (LMICs) where antibiotics are often given out without a prescription. This study presents a hybrid digital Artificial Intelligence(AI) framework aimed at improving rational antibiotic utilization by integrating pharmacies, telemedicine practitioners, and policymakers via an integrated digital infrastructure. The digital intervention system was implemented in 28 community pharmacies and showed significant improvements in how people used antibiotics. For example, the percentage of people who self-medicated with antibiotics dropped from 4.05% to 0.86%, and the percentage of people who bought antibiotics with a prescription jumped from 27.6% to 43.5%. We propose an Artificial Intelligence(AI) framework that combines a machine learning (ML)-based and a large language model (LLM)-based symptom checker for intelligent triage and clinical decision support, based on this foundation. These models will allow for efficient analysis of both structured and narrative symptom data, which will help pharmacists give advice and doctors refer patients right away. The suggested model demonstrates a scalable, data-driven, and human-in-the-loop approach to antibiotic stewardship, with the potential to significantly enhance AMR mitigation and healthcare accessibility in LMIC settings.

Integrating AI-Driven Triage into Digital Pharmacy Systems for Rational Antibiotic Use in Low-Resource Settings.

Explainability, domain generalization, and rare-class reliability are critical challenges in medical AI, where deep models often fail under real-world distribution shifts and exhibit bias against infrequent clinical conditions. This paper introduces XAI-MeD, an explainable medical AI framework that integrates clinically accurate expert knowledge into deep learning through a unified neuro-symbolic architecture. XAI-MeD is designed to improve robustness under distribution shift, enhance rare-class sensitivity, and deliver transparent, clinically aligned interpretations. The framework encodes clinical expertise as logical connectives over atomic medical propositions, transforming them into machine-checkable, class-specific rules. Their diagnostic utility is quantified through weighted feature satisfaction scores, enabling a symbolic reasoning branch that complements neural predictions. A confidence-weighted fusion integrates symbolic and deep outputs, while a Hunt-inspired adaptive routing mechanism— guided by Entropy Imbalance Gain (EIG) and Rare-Class Gini mitigates class imbalance, high intra-class variability, and uncertainty. We evaluate XAI-MeD across diverse modalities, on four challenging tasks: (i) Seizure Onset Zone (SOZ) localization from rs-fMRI, (ii) Diabetic Retinopathy grading, (iii) cardiac function assessment from echocardiograms, and (iv) ECG-based cardiac analysis. Experiments across ten multicenter datasets demonstrate substantial performance improvements, including 6% gains in cross-domain generalization and a rare-class F1 score of 0.916, far outperforming state-of-the-art deep learning baselines (F1 = 0.63). Ablation studies confirm that the clinically grounded symbolic components act as effective regularizers, ensuring robustness to distribution shifts. XAI-MeD thus provides a principled, clinically faithful, and interpretable approach to multimodal medical AI.

XAI-MeD: Explainable Knowledge Guided Neuro-Symbolic Framework for Domain Generalization and Rare Class Detection in Medical Imaging

We present a training-free graph-based approach for solving interactive reasoning tasks in the ARC-AGI-3 benchmark. ARC-AGI-3 comprises game-like tasks where agents must infer task mechanics through limited interactions, and adapt to increasing complexity as levels progress. Success requires forming hypotheses, testing them, and tracking discovered mechanics. The benchmark has revealed that state-of-the-art LLMs are currently incapable of reliably solving these tasks. Our method combines vision-based frame processing with systematic state-space exploration using graph-structured representations. It segments visual frames into meaningful components, prioritizes actions based on visual salience, and maintains a directed graph of explored states and transitions. By tracking visited states and tested actions, the agent prioritizes actions that provide the shortest path to untested state-action pairs. On the ARC-AGI-3 Preview Challenge, this structured exploration strategy solves a median of 30 out of 52 levels across six games and ranks 3rd on the private leaderboard, substantially outperforming frontier LLM-based agents. Beyond its standalone performance, our approach can be viewed as a symbolic external tool: on ARC-AGI-3, it provides an explicit state graph that compensates for current LLMs failure to maintain and exploit such structure, suggesting a concrete way to augment LLM-based reasoners on interactive logical tasks. Taken together, these results indicate that explicit graph-structured exploration, even without learning, can serve as a strong baseline for interactive reasoning and underscore the importance of systematic state tracking and action prioritization in sparse-feedback environments.

Graph-Based Exploration for ARC-AGI-3 Interactive Reasoning Tasks

Alzheimer's disease intervention planning requires both predictive modeling of biomarker trajectories and counterfactual reasoning about treatment timing. We propose a neuro-symbolic architecture that integrates Fourier Neural Operators (FNOs) for physics-informed biomarker prediction with Answer Set Programming (ASP) and SMT solving for verifiable intervention planning. Our approach adapts FNO methods to learn surrogate operators for AT(N) biomarker cascade dynamics, enabling fast multi-year trajectory forecasting while preserving cascade constraints. The symbolic layer formalizes clinical knowledge using first-order logic and temporal logic rules, allowing ASP/s(CASP) to generate candidate intervention strategies that are verified against safety properties using Z3 SMT solving. This combination provides both the predictive power of deep learning and the formal guarantees of symbolic reasoning, addressing critical translational challenges in precision medicine for Alzheimer's disease.

Neuro-Symbolic AI for Alzheimer's Disease: Physics-Informed Biomarker Prediction and Verifiable Intervention Planning

Rising provider turnover results in frequently needing to rematch patients with available providers. However, the rematching process is cumbersome for both patients and health systems, resulting in labor-intensive and ad hoc reassignments. We propose a novel patient-provider matching approach to address this issue by offering patients limited provider menus. The goal is to maximize match quality across the system while preserving patient choice. We frame this as a novel variant of assortment optimization, where patient-specific provider menus are offered upfront, and patients respond in a random sequence to make their selections. This hybrid offline-online setting is understudied in previous literature and captures system dynamics across various domains. We first demonstrate that a greedy baseline policy--which offers all providers to all patients--can maximize the match rate but lead to low-quality matches. Based on this, we construct a set of policies and demonstrate that the best policy depends on problem specifics, such as a patient's willingness to match and the ratio of patients to providers. On real-world data, our proposed policy improves average match quality by 13\% over a greedy solution by tailoring assortments based on patient characteristics. Our analysis reveals a tradeoff between menu size and system-wide match quality, highlighting the value of balancing patient choice with centralized planning.

Assortment Optimization for Patient-Provider Matching

NP-Hard scheduling problems are increasingly more prevalent in our daily lives. Their inherent difficulty and larger scale constitute a real challenge to both Artificial Intelligence and Operational Research communities that continuously seek efficient approaches to solve these theoretical problems and their real-life variants. Existing approaches are either exact or heuristic. Exact algorithms, which generally tackle small-sized academic type scheduling problems, have the property of identifying an optimal solution if one exists in the state-space. Algorithms belonging to the exact class are either time-and-space limited or time-limited. Time-and-space limited algorithms, such as Branch-and-Bound algorithms, have exponential time and space complexities. That is, such algorithms can exhaust the computational memory for a given size of the input problem instance, further confirming their unsuitability for solving very large problem instances. Time-limited algorithms, such as Depth-First Search, have polynomial space complexity and an exponential time complexity. While such algorithms are unlikely to exhaust computational memory, their runtime may be very long time, thus, restricting their suitability to address large-scale problem instances too.

F heuristic search to solve scheduling with deadlines problems

Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial optimization problems is challenging, primal heuristics have been developed to quickly identify high-quality solutions within a short amount of time. Recently, a growing body of research has also used machine learning to accelerate solution methods for challenging combinatorial optimization problems. Despite the increasing popularity of these ML-guided methods, a large body of work has focused on Mixed-Integer Linear Programs (MILPs). MBQPs are challenging to solve due to the combinatorial complexity coupled with nonlinearities. This work proposes ML-guided primal heuristics for Mixed Binary Quadratic Programs (MBQPs) by adapting and extending existing work on ML-guided MILP solution prediction to MBQPs. We introduce a new neural network architecture for MBQP solution prediction and a new training data collection procedure. Moreover, we extend existing loss functions in solution prediction and propose to combine contrastive weighted cross-entropy losses. We evaluate the methods on standard and real-world MBQP benchmarks and show that the developed ML-guided methods significantly outperform existing primal heuristics and state-of-the-art solvers. Furthermore, models trained with our proposed extension with combined losses outperform other ML-based methods adapted from MILPs and improve generalization in cross-regional inference on a real-world wind farm layout optimization problem.

ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs

To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas. The Optimal Power Shutoff (OPS) problem optimizes line energization statuses to manage wildfire ignition risks through de-energizations while reducing load shedding. OPS problems are computationally challenging Mixed-Integer Linear Programs (MILPs) that must be solved rapidly and frequently in operational settings. For a particular power system, OPS instances share a common structure with varying parameters related to wildfire risks, loads, and renewable generation. This motivates the use of Machine Learning (ML) for solving OPS problems by exploiting shared patterns across instances. In this paper, we develop an ML-guided framework that quickly produces high-quality de-energization decisions by extending existing ML-guided MILP solution methods while integrating domain knowledge on the number of energized and de-energized lines. Results on a large-scale realistic California-based synthetic test system show that the proposed ML-guided method produces high-quality solutions faster than traditional optimization methods.

Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk

Reinforcement Learning from Human Feedback (RLHF) has become the dominant paradigm for aligning large language models with human preferences. However, when preference data is aggregated from diverse populations, it remains unclear whether the resulting aligned models serve all demographic groups equitably, or support long-term behavior change and mental health and wellness needs in a balanced way. We investigate this question through a controlled experiment using Direct Preference Optimization (DPO), training on preferences collected from our novel synthetic dataset, the 10th Village, comprising 5,000 synthetic villagers with demographics and personality traits modeled on U.S. Census data and validated psychological instruments. Each villager provided preferences across everyday stressors in financial/employment and social/relationship domains. We introduce an alignment fairness evaluation framework that treats RLHF as a behavior-aware recommendation problem, measuring how well the aligned model matches individual villager preferences compared to the base model and analyzing disparities across demographic subgroups. Our results reveal two critical sources of inequality: First, social and relationship problems receive substantially less benefit from alignment than financial concerns (p < .001), despite already generating higher baseline dissatisfaction. Second, more educated villagers gain disproportionate benefit from alignment (p < .001), particularly for social problems, creating a compounding advantage. These findings suggest that standard RLHF practices may systematically disadvantage certain problem domains and demographic groups, highlighting the need for fairness-aware approaches to preference aggregation and model alignment. Our contributions include both the 10th Village dataset and a reusable evaluation protocol for controlled, behavior-aware fairness research, as well as empirical evidence of disparate impact in preference-based alignment to guide the design of more equitable, wellness-oriented RLHF systems.

Not All Stress Is Treated Equal: Fairness Gaps in AI Support for Everyday Problems

As AI systems increasingly mediate human interactions, most alignment frameworks erroneously assume static human preferences. We introduce Socially-Aware Continual Learning (SCL), a framework that maintains ethical alignment with dynamically evolving norms through norm embeddings and Social Elastic Weight Consolidation (SEWC)—a novel algorithm that adapts regularization strength based on measured norm drift. Extensive experiments on longitudinal datasets demonstrate SCL’s superior balance between Alignment Stability (F1 = 0.84) and Normative Plasticity (F1 = 0.87), significantly outperforming state-of-the-art baselines (p < 0.001). Our contributions include: (1) validated drift detection metrics (BDI, DCS) achieving 0.89 F1-score, (2) human evaluations showing 82% trust recovery after norm shifts, (3) formalized fairness-utility trade-offs with 4% versus 20% disparity for baselines, and (4) societal-scale simulation showing 36% polarization reduction. SCL provides both theoretical stability guarantees and practical tools for developing socially responsive AI.

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Integrating AI-Driven Triage into Digital Pharmacy Systems for Rational Antibiotic Use in Low-Resource Settings.

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