Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical flood models are accurate, their substantial computational cost limits their use in operational settings where rapid predictions are essential. Models designed with graph neural networks (GNNs) provide both speed and accuracy while having the ability to process unstructured spatial domains. Given its flexible input and architecture, GNNs can be leveraged alongside physics-informed techniques with ease, significantly improving interpretability and generalizability. We introduce a novel flood GNN architecture, DUALFloodGNN, which embeds physical constraints at both global and local scales through explicit loss terms. The model jointly predicts water volume at nodes and flow along edges through a shared message-passing framework. To improve performance for autoregressive inference, model training is conducted with a multi-step loss enhanced with dynamic curriculum learning. Compared with standard GNN architectures and state-of-the-art GNN flood models, DUALFloodGNN achieves substantial improvements in predicting multiple hydrologic variables (e.g., water volume, flow, and depth) while maintaining high computational efficiency. The model is open sourced at https://github.com/acostacos/dual_flood_gnn. The dataset is open sourced at https://doi.org/10.25910/9xav-0s86.
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Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: adversarial pattern evolution by malicious actors and the out-of-distribution (OOD) problem caused by varied transaction semantics on blockchains. To address these challenges, we propose a novel framework termed TEmporal Motif-aware Graph Test-Time Adaptation (TEMG-TTA). First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif-aware graph learning. Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real-world datasets demonstrate that our proposed TEMG-TTA outperforms state-of-the-art GAD approaches by an average of 54.88%. A further case study on interpretable motif patterns reveals that TEMG-TTA explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code is publicly available at https://github.com/LuoXishuang0712/TEMG-TTA/.
Joint Species Distribution Modeling (JSDM) is a key enabler for biodiversity monitoring and conservation planning. However, accurate JSDM faces two coupled challenges: environmental drivers and species distributions are inherently spatio-temporal, while species co-occurrence patterns exhibit complex non-linear community structure and severe long-tail imbalance driven by rare species. Existing approaches often address these factors in isolation, learning from static covariates or neglecting the historical trajectories of dynamic community structure. To overcome these limitations, we propose STELLAR (Spatio-Temporal Environmental Learning with Latent Alignment and Refinement), a novel framework that learns a shared latent space where dynamic habitat context and community structure are optimized jointly. Our approach integrates three complementary components: (1) a Graph-Temporal Encoder that employs graph attention and recurrent units to aggregate spatial neighborhood effects and capture the co-evolving historical dynamics of environmental context and community structure; (2) a Context-Anchored Latent Alignment mechanism that structures the latent space using a label-activated mixture prior and supervised contrastive learning, actively clustering species based on shared environmental preferences; and (3) an Imbalance-Aware Decoupled Decoding module that utilizes Asymmetric Loss to focus learning on hard, rare species samples, preventing mode collapse in the long tail. Experiments on the large-scale eBird dataset, curated with domain experts, demonstrate that our framework significantly outperforms state-of-the-art baselines, particularly in predicting rare species and revealing interpretable species interactions.
Multimodal meme understanding is increasingly used to analyze socially sensitive content, yet existing models often exhibit biased behavior when interpreting economic dependence and social roles under ambiguity. Many memes express economic relationships through sparse text or symbolic visual cues, providing insufficient evidence for gendered attribution. In such underspecified settings, models tend to rely on pretraining correlations, leading to hallucinated and stereotypical economic role assignments. In this work, we study gendered economic dependence in image-text memes through the lens of contextual sufficiency and identify epistemic overcommitment—inferring roles without adequate evidence—as a primary source of bias. We propose CGER-Net, a context-grounded multimodal framework that estimates whether the input provides sufficient evidence for gendered economic reasoning and applies evidence-gated inference to enable confident attribution when cues are explicit while favoring principled abstention otherwise. We evaluate CGER-Net on EconMeme-GE, a curated dataset of image-text memes annotated as Men, Women, Neutral, or Ambiguous. Across strong contemporary multimodal baselines, CGER-Net reduces Gender Overcommitment Rate by up to 44% on ambiguous instances while maintaining comparable accuracy on unambiguous cases. Human evaluation further shows that 79% of generated rationales are judged as epistemically aligned with the available evidence. These results highlight the importance of modeling when not to infer for reliable and responsible multimodal analysis.
Sign Language Production (SLP) system translates spoken or written language into sign language, enabling accessible communication between the deaf/hard-of-hearing and the hearing population. Being one of the most widely used sign languages globally, Indian Sign Language (ISL) is a very low-resource language and lacks such SLP systems. This paper presents a scalable and modular SLP framework based on Sign-Pose-VQ-VAE model, designed for low-resource settings. The model learns discrete pose representations (codes) by disentangling body, left-hand, and right-hand keypoints, enabling efficient pose modeling and co-articulated sign generation. The proposed system is evaluated using a Hindi movie subtitle corpus coupled with an off-the-shelf back-translation model and achieves a gloss BLEU-4 score of 47.20. The system-generated signs are evaluated by certified ISL interpreters with an average rating of 4.33/5, and a BERT precision of 0.7683 on glosses. In addition, the proposed system achieves state-of-the-art performance among keypoint-based methods on the PHOENIX14T benchmark, attaining a BLEU-4 score of 10.03 and surpassing the previous best method by 0.67 points. The system with code is available at https://cs.rkmvu.ac.in/~isl/sl_gen_vqvae.
Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as signal control and network-level traffic management. In real-world deployments, forecasting models must operate under structural and observational uncertainties, conditions that are rarely considered in model design. Recent approaches achieve strong short-term predictive performance by tightly coupling spatial and temporal modeling, often at the cost of increased complexity and limited modularity. In contrast, efficient time-series models capture long-range temporal dependencies without relying on explicit network structure. We propose UniST-Pred, a unified spatio-temporal forecasting framework that first decouples temporal modeling from spatial representation learning, then integrates both through adaptive representation-level fusion. To assess robustness of the proposed approach, we construct a dataset based on an agent-based, microscopic traffic simulator (MATSim) and evaluate UniST-Pred under severe network disconnection scenarios. Additionally, we benchmark UniST-Pred on standard traffic prediction datasets, demonstrating its competitive performance against existing well-established models despite a lightweight design. The results illustrate that UniST-Pred maintains strong predictive performance across both real-world and simulated datasets, while also yielding interpretable spatio-temporal representations under infrastructure disruptions. The source code and the generated dataset are available at https://anonymous.4open.science/r/UniST-Pred-EF27.
Accurate simulation of terrestrial ecological carbon cycles is crucial for global climate change and ecosystem management. Process-based carbon models have high interpretability, but suffer from insufficient accuracy and slow computation due to fixed parameters. In contrast, deep-learning carbon models achieve high accuracy, but disregard physical principles, which prevents ecologists from explaining ecosystem dynamics. We propose NeuroDALEC, an interpretable framework that embeds the DALEC carbon-cycle model within a neural network, enabling differentiable computation of ecological processes. Key parameters and ensemble learning strategies are designed, and mass-conserving carbon pool state transition equations are introduced to ensure physical consistency. Experiments show NeuroDALEC outperforms existing models in both accuracy and efficiency. Moreover, it provides sufficient interpretability by predicting all components of the carbon cycle. Deployed in a real-time carbon assimilation system, NeuroDALEC supports daily carbon forecasting and decision-making. This work contributes to the United Nations' Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land). The source code is available at: https://github.com/codesiena/NeuroDALEC.
Reducing preventable maternal mortality remains a global health priority. Under Sustainable Development Goal (SDG) target 3.1, the WHO emphasizes timely and equitable allocation of limited maternal health resources. Motivated by Department of Obstetrics and Gynecology at several important hospitals in Uganda and Ghana, we study the problem of sequential allocation of wearable vital sign monitoring devices among maternal mothers. While deep reinforcement learning (RL) has shown promise for sequential resource allocation, its limited interpretability hinders adoption in such high-stakes settings. In contrast, large language model (LLM) agents provide human-readable reasoning but often struggle with effective long-term decision making. To bridge this gap, we introduce Rule-Bottleneck RL (RBRL), the first LLM agent framework for resource allocation problems that jointly optimizes language-based decision policy and explainability. At each step within RBRL, an LLM first generates candidate rules---language statements capturing decision priorities tailored to the current state. RL then optimizes rule selection to maximize environmental rewards and explainability, with the LLM acting as a judge. Finally, an LLM chooses the action (optimal allocation) based on the rule. We provide conditions for RBRL performance guarantees as well as the finite-horizon evaluation gap of the learned RBRL policy. Experiments in maternal health show that RBRL outperforms baseline LLM agents and approaches the performance of deep RL, while producing clearer, policy-relevant explanations. Human evaluations further confirm improved trust and usability, demonstrating RBRL as a practical AI approach aligned with SDG target 3.1.
Effective flood risk management relies on accurate forecasting, yet the ”black box” nature of state-of-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions. While existing Explainable AI (XAI) methods offer local attributions, they fail to provide the verifiable, operationally meaningful causal narratives required by disaster response authorities. To address this societal challenge, we propose Context-Aware Concept Distillation (CACD), a framework developed in collaboration with domain experts to distill opaque LSTMs into interpretable, hydrology-aware surrogate models. We introduce an unsupervised pipeline to discover a ”Hydrological Language” and a Residual Hypernetwork that dynamically modulates these concepts based on static basin characteristics. Evaluated on 5,203 basins globally, our model achieves high fidelity (Median NSE 0.70), significantly outperforming black-box baselines (e.g., Multi Layer Perceptrons) on unseen future data. By demonstrating that human-interpretable concepts are sufficient to reconstruct flood dynamics, this work balances AI accuracy with the transparency required for responsible environmental decision-making.
Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively high costs and limited experimental opportunities. This paper presents Human-in-the-Loop Meta Bayesian Optimization (HL-MBO), a framework that integrates expert knowledge with few-shot, uncertainty-aware machine learning to accelerate discovery in data-scarce, high-stakes scientific domains. HL-MBO introduces a meta-learned surrogate model with an expert-informed acquisition function to recommend candidate experiments. To foster trust and enable informed decisions, HL-MBO also provides interpretable explanations of its suggestions. We show that HL-MBO outperforms current BO methods for ICF energy yield optimization, as well as benchmarks in molecular optimization and critical-temperature maximization for superconducting materials.
Forecasting climate factors is critical for anticipating agro-climatic risks and enabling sustainable crop management. However, accurate prediction remains challenging due to complex spatiotemporal variability, heterogeneous seasonal patterns, and intricate interdependencies among climate variables. Inspired by agronomic knowledge, We propose DoIGNN, a Domain-Informed Graph Neural Network that injects a domain-structured graph constraint built from Agro-Climatic Homogeneous Zones (ACHZs). Specifically, we partition stations into agro-climatic zones using long-term climatic statistics and location attributes, and construct a hierarchical ACHZ-guided adjacency. To better capture shared climate dynamics, we introduce a spatiotemporal decomposition module with temporal regularization that factorizes the climate tensor into low-rank global temporal bases and station loadings, yielding a compact station-level global component as auxiliary information for target forecasting. Finally, DoIGNN performs forecasting on both the ACHZ-guided and static-dynamic graphs to learn cross-region dependencies. Experiments on real-world climate datasets demonstrate that DoIGNN consistently improves forecasting accuracy over strong baselines while yielding more interpretable spatial dependency patterns that support climate-informed crop management decisions. Cooperating with Ningbo Natural Resources and Planning Big Data Center, the proposed model has been trained and deployed for local data analysis.
Ocean heat extremes, including marine heatwaves and the El Ni\~no–Southern Oscillation (ENSO), exert profound impacts on marine ecosystems and socio-economic stability. Establishing robust early warning systems is critical for proactive risk management; however, conventional predictive models often fail to generalize to the intensifying, non-stationary extremes driven by rapid global warming. This project introduces a novel AI-Ocean Dynamics synergy designed to provide an integrated early warning system. By synthesizing multi-source observations with physics-informed neural networks, it ensures predictions remain constrained by fundamental physical laws. The system forecasts event onset, intensity, duration, and spatial extent while simultaneously attributing the underlying mechanisms, such as ocean advection and air–sea heat exchange. To validate performance, we establish a specialized ocean heat extremes benchmark to assess predictive skill and attribution reliability. Furthermore, the system incorporates an incremental learning mechanism, enabling continuous adaptation to long-term climatic and environmental evolutions. This project advances the development of reliable, interpretable, and adaptive early warning systems, providing a vital tool for informed policy and maritime decision-making.
Understanding and monitoring human behavior in metro stations play an important role in supporting suicide prevention efforts, where early identification of high-risk situations can enable timely intervention. This requires assessing suicide risk from a surveillance video by jointly reasoning about the behavior of each passenger, his/her spatial context, and temporal dynamics. However, this assessment using videos captured by surveillance cameras is challenging, as it demands accurate perception of human motion, understanding of platform geometry, and aggregation of heterogeneous behavioral cues over time. In this work, we formalize the task of Suicide Risk Assessment (SRA) in metro stations and introduce the first interpretable framework that addresses this challenge. Unlike approaches that focus on isolated subtasks or attempt to infer intent directly, our formulation assesses suicide risk from accumulated evidence by incorporating person tracking, activity recognition, semantic segmentation of the platform, and trajectory-driven risk heatmap modeling. By formalizing SRA as a distinct task and benchmarking a complete operational pipeline achieving 83.2% ROC-AUC on real surveillance data, this work highlights the complexity of suicide risk assessment and opens new directions for research on interpretable AI systems for social good.
Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks. Despite their effectiveness, FEMRs remain black-box models, raising concerns about bias, interpretability, and clinical trust. To address this, we propose the first token-level explainability approach for FEMRs. We train a Transformer-based surrogate model on input-output pairs from the FEMR across two prediction tasks, approximating its behavior while preserving temporal dynamics. We identify the most influential tokens, providing insights into how FEMRs leverage different aspects of patient history for predictions. To evaluate clinical relevance, we introduce a novel clinical alignment metric that quantifies the correspondence between the surrogate model’s key tokens and clinically validated features. Our results demonstrate that the surrogate closely approximates FEMR predictions and that token-level explanations align well with clinical knowledge, offering a practical framework for interpretable and trustworthy clinical AI.
Activated PI3Kδ Syndrome (APDS) is a rare genetic immune disorder caused by variants in PIK3CD or PIK3R1, with highly heterogeneous symptoms that often delay diagnosis. Early recognition is hampered by overlapping clinical presentations and limited clinician awareness, motivating systematic, data-driven approaches to detect APDS-associated phenotypic patterns in routine electronic health records. Traditional linear scoring systems cannot capture complex symptom interactions, while deep learning models, though expressive, often lack interpretability. To bridge this gap, we propose Shapley regression, a novel game-theoretic model replacing the linear predictor with a k-additive cooperative game, explicitly modeling co-occurrence of symptoms while maintaining the transparency and convexity of logistic regression. We carry out an empirical study of our lightweight method on eight public biomedical datasets, showing that a 2-additive model with ℓ2 regularization achieves an optimal trade-off between predictive power and noise robustness. We apply it to a real-world cohort of 222 patients, on which Shapley regression accurately distinguished APDS cases from matched controls, confirming and validating phenotypes known to be associated with APDS, and facilitating the exploration of pairwise interactions between symptoms, validated by clinical experts.
Contrastive vision–language pretraining models such as CLIP align images and text in a shared embedding space but do not explicitly model or evaluate the hierarchical semantics common in medical image interpretation. We propose HCE-CLIP (Hierarchical Conditional Energy CLIP), a vision–language pretraining framework that formulates medical image–text alignment as a hierarchical label-conditional energy modeling problem. HCE-CLIP encodes an image series using transformer-based aggregation and aligns it with free-text reports and structured label state prompts across multiple semantic levels. At each level, conditional energy functions favor clinically consistent label states while suppressing contradictory alternatives, enabling uncertainty-aware inference. To assess semantic coherence, we introduce a hierarchical contradiction-based metric that quantifies logical inconsistencies between fine-grained disease predictions and higher-level clinical summaries. Experiments on MIMIC-CXR and other public benchmarks show that HCE-CLIP outperforms existing medical vision–language pretraining methods in seen-label, zero-shot and linear-probe settings, while producing substantially fewer hierarchical contradictions.
Clinical prediction increasingly relies on multi-modal inputs, where reliability and efficiency are crucial for real-world deployment. However, mainstream fusion and MoE gating typically treat all available modalities as uniformly beneficial and allow noisy or weakly informative modalities to perturb routing, leading to instability, routing collapse, and miscalibrated confidence under missingness and shift. We propose QA-MoE, a Quality-Aware and stable multimodal Mixture-of-Experts that decouples reliability estimation from routing to enable robust, sparse, and interpretable fusion. QA-MoE adopts a modular architecture where each modality is initially encoded into a shared embedding space. To deal with structurally missing data, we employ a completion pathway that maintains a consistent interface. Unlike standard approaches, QA-MoE separates reliability estimation from the routing process. We propose an Evidential Quality Scorer to measure epistemic uncertainty, which then guides a Stability-Enhanced Subset Selector to filter out noisy modalities on the fly. Additionally, we include a Ternary Expert Aggregation mechanism acting as a specialized branch to stabilize predictions when data missingness is severe. Evaluations on clinical benchmarks (ADNI for Alzheimer’s staging and MIMIC-IV for Length-of-Stay) demonstrate that QA-MoE outperforms strong multimodal baselines, improving reliability while cutting down unnecessary computation. This indicates that QA-MoE offers a robust solution for multimodal decision support, especially in clinical settings prone to noise and missing data.
Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or learning-based, often generate redundant or irrelevant edges due to the noisy nature of EEG data. This significantly impairs the quality of graph representations and limits downstream task performance. Motivated by the remarkable reasoning and contextual understanding capabilities of large language models (LLMs), we explore the idea of using LLMs as graph edge refiners. Specifically, we propose a two-stage framework: we first verify that LLM-based edge refinement can effectively identify and remove redundant connections, leading to significant improvements in seizure detection accuracy and more meaningful graph structures. Building on this insight, we further develop a robust solution where the initial graph is constructed using a transformer-based edge predictor and multilayer perceptron, assigning probability scores to potential edges and applying a threshold to determine their existence. The LLM then acts as an edge set refiner, making informed decisions based on both textual and statistical features of node pairs to validate the remaining connections. Extensive experiments on TUSZ dataset demonstrate that our LLM-refined graph learning framework not only enhances task performance but also yields cleaner and more interpretable graph representations.
In multi-turn interactions, large language models (LLMs) often exhibit a persistent influence from prior turns, even after an explicit topic switch. This behavior, which we term semantic inertia, can cause responses to deviate from the expected output distribution for an independent task, undermining task isolation and reliability. This paper introduces a rigorous experimental framework to systematically characterize the nature, form, and dynamics of semantic inertia. We propose an operational definition and a causal-contrastive method that isolates semantic carryover from confounding factors like context length. Through a series of experiments on five leading LLMs, we (i) confirm the existence of semantic inertia and identify its boundary conditions; (ii) model its decay over the course of generation, revealing a characteristic timescale and a heavy-tailed distribution; (iii) decompose its effects on three distinct channels—factual accuracy, structural integrity, and stylistic expression; and (iv) probe its controllability using prompt-based interventions. Our key findings show that inertia is not a simple length effect but an intrinsic dynamic, strongest in the initial part of a generation and decaying over a timescale of approximately 100-200 tokens. Its impact is most pronounced as a stylistic residue, while its effect on factual correctness is weaker and highly dependent on the task and domain switch. Crucially, we find that prompt-level ``reset'' instructions are unreliable and often counter-productive, while conflicting constraints consistently amplify, rather than resolve, output deviation. These results suggest that governing semantic inertia requires system-level state management mechanisms rather than relying on prompt engineering alone.
Cross-modal brain networks characterize the complex connections between different brain regions from both functional and structural perspectives, which is of significant importance for brain network analysis and the diagnosis of brain diseases. However, existing methods have failed to fully exploit the complementary information between functional and structural brain networks, neglecting the guiding role of topology in the transmission of functional modal information, as well as the potential associations of cross-modal high-order information. To address these challenges, this paper proposes a cross-modal dynamic hypergraph computing (CDHGC) framework for brain disease diagnosis and an in-depth analysis of coupled functional-structural brain networks. The CDHGC comprises two key modules: the topology-guided dynamic hypergraph generation module, which combines the topology-constrained KNN and topology-guided centroid sampling methods to uncover high-order correlations among brain regions across modalities based on topological structure. This module dynamically optimizes the hypergraph structure during training to explore potential associations of cross-modal information. The cross-modal hypergraph convolution module efficiently aggregates information from functional-structural brain networks through a cross-modal attention mechanism during message-passing, resulting in high-order joint representations. Experiments conducted on the ADNI and ABIDE datasets demonstrate that the proposed method outperforms current state-of-the-art approaches. Interpretability analysis reveals that the proposed method can discover multi-modal biomarkers associated with brain diseases.