Temporal knowledge graph (TKG) reasoning is critical for modeling and forecasting the evolution of real-world events. Existing TKG construction pipelines transform raw text into structured temporal quadruples as graph facts. However, in this process, they often fail to preserve reasoning-relevant contextual semantics from the original corpus that cannot be explicitly represented as graph facts, leaving such implicit contextual information unused by current temporal reasoning models. To address this limitation, we propose a Textual-Temporal Graph Fusion Network (TTGFN), a context-aware framework that leverages implicit contexts from text via LLM-based semantic encoding and fuses it with structure-constrained temporal graph representations for reasoning. To the best of our knowledge, this is the first work to systematically leverage LLMs to reuse previously overlooked implicit contextual information and incorporate it into temporal knowledge graph reasoning, substantially improving model performance. Extensive experiments conducted on three TKG reasoning benchmark datasets demonstrate that TTGFN outperforms the state-of-the-art approaches, with Hits@1 gains of 20.85% on ICEWS14 dataset, 31.14% on ICEWS05-15 dataset, and 24.22% on ICEWS18, respectively.
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Meteorological downscaling is crucial for high-resolution regional climate forecasting and disaster early warning. While neural operators have emerged as a promising paradigm for modeling complex spatiotemporal mappings, existing frameworks often struggle with spherical manifold geometric distortions, inherent atmospheric multi-scale coupling mismatches, and lack of explicit atmospheric laws. We propose the Spherical Physics-informed Neural Operator, which utilizes a Spherical Laplacian Decomposition to partition atmospheric fields into hierarchical frequency components, maintaining exact point-wise correspondence across scales. To evaluate these representations at arbitrary locations, we introduce a localized spherical integral operator that approximates continuous kernel transforms via geometry-aware attention. Dynamical consistency is further enforced by embedding differentiable constraints into the learning process. Extensive experiments demonstrate that our framework attains superior accuracy and zero-shot generalization across various meteorological variables and unseen queries, representing a robust and interpretable solution for global-to-regional meteorological downscaling.
Venous thromboembolism (VTE) is a common and life-threatening complication in cancer patients after treatment. Early risk assessment and detection of VTE primarily rely on clinical indicators, such as blood test results. However, existing studies are limited to static or snapshot-based models, failing to capture the evolving dynamics of disease progression, as deep time-series modeling is hindered by the lack of longitudinal clinical data. To address this gap, we introduce CliTsVTE, a large-scale clinical time-series dataset curated for VTE modeling, comprising 501,063 samples from 26,022 patients over seven years across nine cancer types. The dataset contains continuous time gaps between consecutive time points. Unlike many benchmarks, CliTsVTE reflects real-world clinical settings and presents unique challenges in continuous irregular time-series modeling with long-term irregularity and varying data granularity, which makes missingness significantly consequential. To tackle this, we propose a deep learning framework integrating multiple sequential backbones with an adversarially regularized autoencoder (ARAE) that learns latent representations to eliminate missingness. Experiments on CliTsVTE show that our best model achieves 88.7% accuracy and an AUC of 0.952, significantly outperforming traditional time-point models and regular time-series benchmarks. These results establish a strong benchmark for deep modeling of continuous irregularity in clinical time-series data and highlight the potential of AI-driven large-scale clinical datasets in solving real-world medical research challenges.
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.
Anti-money laundering (AML) systems are important for safe economic trade and for the fight against financial crime. Recently, a number of AML algorithms based on graph neural networks (GNNs) and graph transformers (GTs) have been proposed. Compared to traditional machine learning solutions, these methods have been shown to achieve significantly better detection results. Yet, the state-of-the-art AML algorithms have a key limitation: they fail to jointly address money laundering classification and money laundering sub-network discovery, despite their strong theoretical connection. To bridge this gap, we propose a translation-based AML system (TAML) that is capable of jointly solving both problems within the same latent space. Our extensive experimental evaluation on multiple datasets demonstrates the superiority of TAML over the state-of-the-art in both tasks.
Reinforcement learning (RL) policies often outperform classical controllers in simulation, yet rarely reach production in safety-critical processes. The barrier is that there is no principled way to answer “Is this policy safe to deploy?” with statistical guarantees. We introduce DEPLOY-RL, a post-training certification framework built on one key insight: deployment certification requires discovering where failures occur (boundary discovery), not measuring how much everywhere (uniform reconstruction). Our contributions: (1) a contract-coupled acquisition function that concentrates sampling on certification-critical boundaries, achieving ≈ 2× sample efficiency with a semi-empirical ambiguity reduction bound (domain-calibrated convergence guarantee); (2) conformal risk control providing finite-sample false-go guarantees (≤ α) under explicit deployment contracts; (3) a three-way decision framework (Deploy/No-Deploy/Abstain) with fail-safe PID/MPC fallback. In simulations on papermaking (industrial digital twin) and Tennessee Eastman (public benchmark), DEPLOY-RL achieves 4.4% false-go rate (vs. 8.6% for the best baseline) while retaining 88.6% policy coverage—the only method achieving <5% false-go with >85% coverage among 14 baselines under our evaluation protocol.
Dynamically Reconfigurable Battery (DRB) systems employ power electronic switches to create dynamic topologies. They enable effective management of cell difference through real-time adjustment of cell connections. However, existing DRB control methods struggle to learn effective strategies due to sparse rewards, which arise from blind exploration in large topological action spaces and complex operational constraints. This leads to insufficient policy learning, making safety and balancing performance difficult to ensure in practical applications. To this end, we propose a Subgraph-Augmented Hierarchical Reinforcement Learning (SAHRL) framework. By combining hierarchical policies with topological structural knowledge, SAHRL effectively accelerates policy exploration and mitigates reward sparsity. Specifically, the high-level policy determines the strategic direction, while the subgraph-augmented low-level policy refines actions to meet operational constraints. The topological structural knowledge, extracted in the form of subgraphs and incorporated as an inductive bias, helps the agent focus on meaningful action patterns and reduce invalid exploration in the large action space. Extensive simulations and real-world experiments show that SAHRL achieves safe and efficient balancing. Notably, it increases the energy release by 10.56% compared to conventional methods in real-world applications.
Forecasting Origin-Destination (OD) demand for new metro lines is critical for sustainable infrastructure planning but faces spatiotemporal out-of-distribution challenges. Existing models often struggle to capture heterogeneous interaction patterns in changing topologies and overlook inherent uncertainty and over-dispersion issues. To bridge these gaps, we propose PROB-EMOE, a planning-oriented probabilistic framework tailored for network expansion. To ensure robust generalization, we design a Mixture-of-Experts (MoE) predictor that integrates diverse expert views to capture heterogeneous demand patterns across changing topologies. To quantify extrapolation uncertainty, our framework functions as a unified probabilistic system by synergizing Deep Ensembles with a probabilistic output, effectively quantifying both data and model uncertainty. Through a systematic investigation of likelihood families, we empirically demonstrate that the Negative Binomial distribution offers the optimal fit in this context. Extensive experiments on a multi-year Shenzhen metro dataset demonstrate that our approach achieves state-of-the-art predictive performance and provides the sharpest calibrated uncertainty intervals. The framework has been deployed in a metropolitan smart-data platform to support risk-aware investment decisions.
With the rapid expansion of photovoltaic (PV) power generation worldwide, PV systems have become key to global energy construction. Accurate PV forecasting is essential for safe grid operation and renewable energy integration. However, most existing models rely heavily on site-specific historical data and perform poorly when deployed in cold-start scenarios of newly built power plants. We propose PhysTrans, a physics-aware transferable framework for cold-start PV forecasting. Firstly, we design a physics-constrained residual network that utilizes a clear-sky module for better physical consistency. Furthermore, we propose a dynamic cloud cropping method to obtain the cloud information of shaded PV stations by fitting the angle of the sun offsets. To fuse the asymmetric data, a query-based asymmetric fusion mechanism is introduced to achieve high-precision alignment of multi-modal data. We conduct experiments on global datasets, and the results show that the PhysTrans outperforms state-of-the-art models with a 13.2% decrease in MAE in the single-site task, and also outperforms existing migration models with an average decrease in MAE of 12.7% in the cross-sites task. Our work advances reliable and transferable PV forecasting for early-stage grid integration and contributes to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), in line with the Leave No One Behind principle.
Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios. This gap is critical in regions like rural India, where patients often express complex medical queries in native Indic languages and rely on multimodal inputs such as medical images. Existing English-centric MLLMs struggle to support such use cases, limiting equitable access to AI-driven healthcare assistance. To address this challenge, we introduce ArogyaBodha, a large-scale multilingual multimodal medical question--answer dataset constructed from eight heterogeneous sources, covering 31 body systems, six imaging modalities, and 21 clinical domains across English and seven major Indian languages. We further propose ArogyaSutra, an actor--critic-based multi-agent framework that integrates tool grounding with dual-memory mechanisms for step-wise, reasoning-aware decision making, and uses stored actor--critic simulation trajectories for distillation. Experiments show that our dataset and framework improve multilingual medical reasoning accuracy across all Indic languages, with ablations validating the contribution of each component. The source code and dataset are available at: https://iitp-cse.github.io/ArogyaSutra/
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/.
Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalable alternative, its performance in the wild is often challenged by environmental variability. Current methods rely heavily on cloud resource, which requires continuous uploading of field data for model retraining. This approach is unsuitable for remote deployments because it consumes limited power and network connectivity. To address these constraints, this research proposes a shift from model adaptation to knowledge adaptation. We introduce an architecture that separates visual perception from reasoning, combining a visual encoder with a dynamic knowledge base. We uses an explicit knowledge base to replace implicitly encoding expert knowledge into model parameters. This method also supports knowledge sustainability by preserving expert insights in a structured form. Through cross-disciplinary collaboration with biologists and Indigenous communities, this work advances ethical AI co-development, fostering responsible and culturally informed ecosystem management.
Educational recommendations empower personalized learning by suggesting suitable learning resources to learners, and the graph-based recommenders are widely adopted. Existing methods are mainly ID-based, which initialize learners and resources with trainable identifiers and optimize their representations solely from the interaction graph. As a result, the lack of semantic understanding of learning resources and learning paths hinders further improvements in recommendation accuracy. To alleviate the problem, we propose KLU4EduRec, which leverages large language models (LLMs) to understand resource knowledge and learning paths, thereby enhancing traditional graph-based educational recommenders. Specifically, for learning path understanding, we segment a learning path by detecting learning pattern drift in resource knowledge sequence, and prompt LLMs to infer learners' learning patterns within each segment. The segment-level patterns are then chronologically aggregated to represent the overall learning path. Besides, we prompt LLMs to summarize the core knowledge of learning resources from their content as complementary semantic signals. Finally, the resulting semantic representations are aligned and fused with structural representations learned by a graph-based recommender to enable more accurate recommendations. Extensive experiments show that KLU4EduRec greatly outperforms existing methods, including traditional ID-based methods and recent LLM-powered methods. A case study shows how the understanding of pattern drift in a learning path leads to more suitable recommendations. Our codes are available at https://github.com/DaSESmartEdu/KLU4EduRec.
Many high-stakes social applications of AI, such as public health surveillance and policy planning, operate at the community- rather than individual-level. However, most AI fairness research operates at the individual- or data-level (i.e. document or image) and rely on metrics defined over discrete demographic categories rather than population-level demographic proportions. In this work, we first introduce the Bilateral Concentration Index (BCI) to quantify nonmonotonic error disparities missed by the category-based metrics used at individual or data-levels. Then we conduct a large-scale audit of sociodemographic error disparities in both lexical- and transformer-based models of county-level health outcomes, over a dataset cover billions of community-mapped messages. While all tasks had significant disparity, the size varied widely depending on the outcome and model, from BCI of 2.1% for predicting life satisfaction to 17.0% for predicting fair or poor health. We further evaluate four approaches for incorporating sociodemographic information, as potential bias mitigation strategies, finding that while demographic inclusion consistently improved predictive accuracy, it frequently amplified error disparities. The largest disparities were associated with education and income (BCI = 2.7–16.4%), often reducing accuracy for low-income, and in some cases, high-income communities. These findings highlight a critical accuracy–fairness trade-off in community-level models for public health tasks, demonstrating how seemingly beneficial modeling choices can lead to increased disparities which could disadvantage communities if model predictions are used to make policy decisions in geographic areas where this public health data is not available.
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.
Recent research shows that conversational AI can shift voter preferences, with effects persisting for weeks. Yet frontier models exhibit a documented "persuasion-reliability tradeoff", producing hallucinated or systematically distorted election information. Despite these risks, election officials lack standardized tools to systematically evaluate AI systems before deployment. We propose CivicAudit-Bench, a stakeholder-guided auditing framework to stress-test large language models for civic hallucinations, false confidence, jurisdiction-dependent failure, and asymmetric refusals/accuracy. This framework introduces a modular, counterfactual, and severity-aware auditing methodology that integrates roll-call–based alignment modeling, entity-swap probing, and jurisdiction-conditional correctness criteria. Informed by engagement with the U.S. Election Assistance Commission, the toolkit consists of three modules: (1) PoliBias-US, a multi-indicator alignment screen combining Congressional roll-call ideology scaling with party-cue counterfactual sensitivity, persona robustness, and narrative-framing alignment; (2) HalluBias-Election, an evidence-linked benchmark that measures hallucinations, severity-weighted critical errors, and asymmetries via Entity-Swap Counterfactual Probing and a jurisdiction-safe completion criterion; and (3) Disclosure-Test, pre-registered experiments assessing whether transparency and calibrated-uncertainty disclosures reduce overreliance and attenuate persuasion without blocking legitimate civic information. CivicAudit-Bench outputs versioned audit scorecards and a coordinated white-hat disclosure workflow, advancing UN SDG~16 by strengthening democratic information integrity.
Motivated by the problem of assigning mediators to cases in the Kenyan judicial system, we study an online resource allocation problem where incoming tasks (cases) must be immediately assigned to available, capacity-constrained resources (mediators). The resources differ in their quality, which may need to be learned. In addition, resources can only be assigned to a subset of tasks that overlaps to varying degrees with the subset of tasks other resources can be assigned to. The objective is to maximize task completion while satisfying soft capacity constraints across all the resources. The scale of the real-world problem poses substantial challenges, since there are over 2000 mediators, and a multitude of combinations of geographic locations (87) and case types (12) that each mediator is qualified to work on. Together, these features—unknown quality of new resources (newly onboarded mediators), soft capacity constraints (due to the mandate to assign cases without delay), and high-dimensional state space—make existing scheduling and resource allocation algorithms either inapplicable or inefficient. We formalize the problem in a tractable manner, using a quadratic program formulation for assignment and a multi-agent bandit style framework for learning. We demonstrate the key properties and advantages of our new algorithm, SMaRT (Selecting Mediators that are Right for the Task), compared with baselines on some stylized instances of the mediator allocation problem. We then turn to considering its application to real-world data on cases and mediators from the Kenyan Judiciary. SMaRT outperforms baselines and allows for controlling the tradeoff between the strictness of the capacity constraints and overall case resolution rates, both in situations where mediator quality is known beforehand and when the problem is bandit-like in that learning is part of the problem definition. On the strength of these results, we plan to conduct a randomized controlled trial in which we deploy SMaRT in the Judiciary's mediation management system.
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.
Climate policy analysis requires models that capture multi-gas climate effects, but such models are too slow to embed in reinforcement learning loops at scale. In collaboration with a pan-European public-sector environmental agency, we develop a multi-agent reinforcement learning (MARL) framework that integrates a higher-fidelity climate surrogate as the environment transition, enabling regional agents to learn policies under multi-gas dynamics. We train a recurrent surrogate on 20,000 multi-gas emission pathways to emulate CICERO-SCM. The surrogate achieves near-simulator accuracy (global-mean temperature RMSE ≈ 4 × 10⁻⁴ K) with ∼1000× faster one-step inference and yields >100× end-to-end MARL training speed-up. We show policy agreement with the simulator in tractable settings and propose a replay- and rank-consistency test (Kendall’s τ) for assessing policy fidelity when simulator-in-the-loop training is infeasible. This enables large-scale multi-agent policy experiments while retaining high-fidelity multi-gas climate response.
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.