Large language models (LLMs) can amplify misinformation, undermining societal goals like the UN SDGs. We study three documented drivers of misinformation (valence framing, information overload, and oversimplification) which are often shaped by one's default beliefs. Building on evidence that LLMs encode such defaults (e.g., "joy is positive," "math is complex") and can act as "bags of heuristics," we ask: can general belief-driven heuristics behind misinformative behaviour be recovered from LLMs as clear rules? A key obstacle is that global rule-extraction methods in explainable AI (XAI) are built for numerical inputs/outputs, not text. We address this by eliciting global LLM beliefs and mapping them to numerical scores via statistically reliable abstractions, thereby enabling off-the-shelf global XAI to detect belief-related heuristics in LLMs. To obtain ground truth, we hard-code bias-inducing nonlinear heuristics of increasing complexity (univariate, conjunctive, nonconvex) into popular LLMs (ChatGPT and Llama) via system instructions. This way, we find that RuleFit under-detects non-univariate biases, while global SHAP better approximates conjunctive ones but does not yield actionable rules. To bridge this gap, we propose RuleSHAP, a rule-extraction algorithm that couples global SHAP-value aggregations with rule induction to better capture non-univariate bias, improving heuristics detection over RuleFit by +94% (MRR@1) on average. Our results provide a practical pathway for revealing belief-driven biases in LLMs.

Recent advances in text-to-video (T2V) generation have enabled the creation of high-fidelity, temporally coherent clips from natural language prompts. Yet these systems come with significant computational costs, and their energy demands remain poorly understood. In this paper, we present a systematic study of the latency and energy consumption of state-of-the-art open-source T2V models. We first develop a compute-bound analytical model that predicts scaling laws with respect to spatial resolution, temporal length, and denoising steps. We then validate these predictions through fine-grained experiments on WAN2.1-T2V, showing quadratic growth with spatial and temporal dimensions, and linear scaling with the number of denoising steps. Finally, we extend our analysis to six diverse T2V models, comparing their runtime and energy profiles under default settings. Our results provide both a benchmark reference and practical insights for designing and deploying more sustainable generative video systems.

Disinformation is among the top risks of generative artificial intelligence (AI) misuse. Global adoption of generative AI necessitates red-teaming evaluations (i.e., systematic adversarial probing) that are robust across diverse languages and cultures, but red-teaming datasets are commonly US- and English-centric. To address this gap, we propose "anecdoctoring", a novel red-teaming approach that automatically generates adversarial prompts across languages and cultures. We collect misinformation claims from fact-checking websites in three languages (English, Spanish, and Hindi) and two geographies (US and India). We then cluster individual claims into broader narratives and characterize the resulting clusters with knowledge graphs, with which we augment an attacker LLM. Our method produces higher attack success rates and offers interpretability benefits relative to few-shot prompting. Results underscore the need for disinformation mitigations that scale globally and are grounded in real-world adversarial misuse.

Under a unified operational definition, we define LLM memory as a persistent state written during pretraining, finetuning, or inference that can later be addressed and that stably influences outputs. We propose a four-part taxonomy (parametric, contextual, external, procedural/episodic) and a memory quadruple (location, persistence, write/access path, controllability). We link mechanism, evaluation, and governance via the chain write -> read -> inhibit/update. To avoid distorted comparisons across heterogeneous setups, we adopt a three-setting protocol (parametric only, offline retrieval, online retrieval) that decouples capability from information availability on the same data and timeline. On this basis we build a layered evaluation: parametric (closed-book recall, edit differential, memorization/privacy), contextual (position curves and the mid-sequence drop), external (answer correctness vs snippet attribution/faithfulness), and procedural/episodic (cross-session consistency and timeline replay, E MARS+). The framework integrates temporal governance and leakage auditing (freshness hits, outdated answers, refusal slices) and uncertainty reporting via inter-rater agreement plus paired tests with multiple-comparison correction. For updating and forgetting, we present DMM Gov: coordinating DAPT/TAPT, PEFT, model editing (ROME, MEND, MEMIT, SERAC), and RAG to form an auditable loop covering admission thresholds, rollout, monitoring, rollback, and change audits, with specs for timeliness, conflict handling, and long-horizon consistency. Finally, we give four testable propositions: minimum identifiability; a minimal evaluation card; causally constrained editing with verifiable forgetting; and when retrieval with small-window replay outperforms ultra-long-context reading. This yields a reproducible, comparable, and governable coordinate system for research and deployment.

Porous materials exhibit vast structural diversity and support critical applications in gas storage, separations, and catalysis. However, predictive modeling remains challenging due to the multiscale nature of structure-property relationships, where performance is governed by both local chemical environments and global pore-network topology. These complexities, combined with sparse and unevenly distributed labeled data, hinder generalization across material families. We propose the Interaction Topological Transformer (ITT), a unified data-efficient framework that leverages novel interaction topology to capture materials information across multiple scales and multiple levels, including structural, elemental, atomic, and pairwise-elemental organization. ITT extracts scale-aware features that reflect both compositional and relational structure within complex porous frameworks, and integrates them through a built-in Transformer architecture that supports joint reasoning across scales. Trained using a two-stage strategy, i.e., self-supervised pretraining on 0.6 million unlabeled structures followed by supervised fine-tuning, ITT achieves state-of-the-art, accurate, and transferable predictions for adsorption, transport, and stability properties. This framework provides a principled and scalable path for learning-guided discovery in structurally and chemically diverse porous materials.

Detailed structural building information is used to estimate potential damage from hazard events like cyclones, floods, and landslides, making them critical for urban resilience planning and disaster risk reduction. However, such information is often unavailable in many small island developing states (SIDS) in climate-vulnerable regions like the Caribbean. T o address this data gap, we present an AIdriven workflow to automatically infer rooftop attributes from high-resolution satellite imagery, with Saint Vincent and the Grenadines as our case study. Here, we compare the utility of geospatial foundation models combined with shallow classifiers against fine-tuned deep learning models for rooftop classification. Furthermore, we assess the impact of incorporating additional training data from neighboring SIDS to improve model performance. Our best models achieve F1 scores of 0.88 and 0.83 for roof pitch and roof material classification, respectively. Combined with local capacity building, our work aims to provide SIDS with novel capabilities to harness AI and Earth Observation (EO) data to enable more efficient, evidence-based urban governance.

Monitoring ground displacement is crucial for urban infrastructure stability and mitigating geological hazards. However, forecasting future deformation from sparse Interferometric Synthetic Aperture Radar (InSAR) time-series data remains a significant challenge. This paper introduces a novel deep learning framework that transforms these sparse point measurements into a dense spatio-temporal tensor. This methodological shift allows, for the first time, the direct application of advanced computer vision architectures to this forecasting problem. We design and implement a hybrid Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) model, specifically engineered to simultaneously learn spatial patterns and temporal dependencies from the generated data tensor. The model's performance is benchmarked against powerful machine learning baselines, Light Gradient Boosting Machine and LASSO regression, using Sentinel-1 data from eastern Ireland. Results demonstrate that the proposed architecture provides significantly more accurate and spatially coherent forecasts, establishing a new performance benchmark for this task. Furthermore, an interpretability analysis reveals that baseline models often default to simplistic persistence patterns, highlighting the necessity of our integrated spatio-temporal approach to capture the complex dynamics of ground deformation. Our findings confirm the efficacy and potential of spatio-temporal deep learning for high-resolution deformation forecasting.

Spatio-temporal data abounds in domain like traffic and environmental monitoring. However, it often suffers from missing values due to sensor malfunctions, transmission failures, etc. Recent years have seen continued efforts to improve spatio-temporal data imputation performance. Recently diffusion models have outperformed other approaches in various tasks, including spatio-temporal imputation, showing competitive performance. Extracting and utilizing spatio-temporal dependencies as conditional information is vital in diffusion-based methods. However, previous methods introduce error accumulation in this process and ignore the variability of the dependencies in the noisy data at different diffusion steps. In this paper, we propose AdaSTI (Adaptive Dependency Model in Diffusion-based Spatio-Temporal Imputation), a novel spatio-temporal imputation approach based on conditional diffusion model. Inside AdaSTI, we propose a BiS4PI network based on a bi-directional S4 model for pre-imputation with the imputed result used to extract conditional information by our designed Spatio-Temporal Conditionalizer (STC)network. We also propose a Noise-Aware Spatio-Temporal (NAST) network with a gated attention mechanism to capture the variant dependencies across diffusion steps. Extensive experiments on three real-world datasets show that AdaSTI outperforms existing methods in all the settings, with up to 46.4% reduction in imputation error.

Machine learning surrogates are increasingly employed to replace expensive computational models for physics-based reliability analysis. However, their use introduces epistemic uncertainty from model approximation errors, which couples with aleatory uncertainty in model inputs, potentially compromising the accuracy of reliability predictions. This study proposes a Gauss-Hermite quadrature approach to decouple these nested uncertainties and enable more accurate reliability analysis. The method evaluates conditional failure probabilities under aleatory uncertainty using First and Second Order Reliability Methods and then integrates these probabilities across realizations of epistemic uncertainty. Three examples demonstrate that the proposed approach maintains computational efficiency while yielding more trustworthy predictions than traditional methods that ignore model uncertainty.

In this paper, we propose a novel Spatial Balance Attention block for spatiotemporal forecasting. To strike a balance between obeying spatial proximity and capturing global correlation, we partition the spatial graph into a set of subgraphs and instantiate Intra-subgraph Attention to learn local spatial correlation within each subgraph; to capture the global spatial correlation, we further aggregate the nodes to produce subgraph representations and achieve message passing among the subgraphs via Inter-subgraph Attention. Building on the proposed Spatial Balance Attention block, we develop a multiscale spatiotemporal forecasting model by progressively increasing the subgraph scales. The resulting model is both scalable and able to produce structured spatial correlation, and meanwhile, it is easy to implement. We evaluate its efficacy and efficiency against the existing models on real-world spatiotemporal datasets from medium to large sizes. The experimental results show that it can achieve performance improvements up to 7.7% over the baseline methods at low running costs.