The impressive capabilities of deep learning models are often counterbalanced by their inherent opacity, commonly termed the "black box" problem, which impedes their widespread acceptance in high-trust domains. In response, the intersecting disciplines of interpretability and explainability, collectively falling under the Explainable AI (XAI) umbrella, have become focal points of research. Although these terms are frequently used as synonyms, they carry distinct conceptual weights. This document offers a comparative exploration of interpretability and explainability within the deep learning paradigm, carefully outlining their respective definitions, objectives, prevalent methodologies, and inherent difficulties. Through illustrative examinations of the MNIST digit classification task and IMDB sentiment analysis, we substantiate a key argument: interpretability generally pertains to a model's inherent capacity for human comprehension of its operational mechanisms (global understanding), whereas explainability is more commonly associated with post-hoc techniques designed to illuminate the basis for a model's individual predictions or behaviors (local explanations). For example, feature attribution methods can reveal why a specific MNIST image is recognized as a '7', and word-level importance can clarify an IMDB sentiment outcome. However, these local insights do not render the complex underlying model globally transparent. A clear grasp of this differentiation, as demonstrated by these standard datasets, is vital for fostering dependable and sound artificial intelligence.

The growing importance of environmental, social, and governance data in regulatory and investment contexts has increased the need for accurate, interpretable, and internationally aligned representations of non-financial risks, particularly those reported in unstructured news sources. However, aligning such controversy-related data with principle-based normative frameworks, such as the United Nations Global Compact or Sustainable Development Goals, presents significant challenges. These frameworks are typically expressed in abstract language, lack standardized taxonomies, and differ from the proprietary classification systems used by commercial data providers. In this paper, we present a semi-automatic method for constructing structured knowledge representations of environmental, social, and governance events reported in the news. Our approach uses lightweight ontology design, formal pattern modeling, and large language models to convert normative principles into reusable templates expressed in the Resource Description Framework. These templates are used to extract relevant information from news content and populate a structured knowledge graph that links reported incidents to specific framework principles. The result is a scalable and transparent framework for identifying and interpreting non-compliance with international sustainability guidelines.

Randomized smoothing is a well-established method for achieving certified robustness against l2-adversarial perturbations. By incorporating a denoiser before the base classifier, pretrained classifiers can be seamlessly integrated into randomized smoothing without significant performance degradation. Among existing methods, Diffusion Denoised Smoothing - where a pretrained denoising diffusion model serves as the denoiser - has produced state-of-the-art results. However, we show that employing a denoising diffusion model introduces a covariate shift via misestimation of the added noise, ultimately degrading the smoothed classifier's performance. To address this issue, we propose a novel adversarial objective function focused on the added noise of the denoising diffusion model. This approach is inspired by our understanding of the origin of the covariate shift. Our goal is to train the base classifier to ensure it is robust against the covariate shift introduced by the denoiser. Our method significantly improves certified accuracy across three standard classification benchmarks - MNIST, CIFAR-10, and ImageNet - achieving new state-of-the-art performance in l2-adversarial perturbations. Our implementation is publicly available at https://github.com/ahedayat/Robustifying-DDS-Against-Covariate-Shift

Cultural competence, defined as the ability to understand and adapt to multicultural contexts, is increasingly vital for large language models (LLMs) in global environments. While several cultural benchmarks exist to assess LLMs' cultural competence, current evaluations suffer from fragmented taxonomies, domain specificity, and heavy reliance on manual data annotation. To address these limitations, we introduce CultureSynth, a novel framework comprising (1) a comprehensive hierarchical multilingual cultural taxonomy covering 12 primary and 130 secondary topics, and (2) a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs. The CultureSynth-7 synthetic benchmark contains 19,360 entries and 4,149 manually verified entries across 7 languages. Evaluation of 14 prevalent LLMs of different sizes reveals clear performance stratification led by ChatGPT-4o-Latest and Qwen2.5-72B-Instruct. The results demonstrate that a 3B-parameter threshold is necessary for achieving basic cultural competence, models display varying architectural biases in knowledge processing, and significant geographic disparities exist across models. We believe that CultureSynth offers a scalable framework for developing culturally aware AI systems while reducing reliance on manual annotation\footnote{Benchmark is available at https://github.com/Eyr3/CultureSynth.}.

Anomaly detection in graph-structured data is an inherently challenging problem, as it requires the identification of rare nodes that deviate from the majority in both their structural and behavioral characteristics. Existing methods, such as those based on graph convolutional networks (GCNs), often suffer from over-smoothing, which causes the learned node representations to become indistinguishable. Furthermore, graph reconstruction-based approaches are vulnerable to anomalous node interference during the reconstruction process, leading to inaccurate anomaly detection. In this work, we propose a novel and holistic anomaly evaluation framework that integrates three key components: a local-global Transformer encoder, a memory-guided reconstruction mechanism, and a multi-scale representation matching strategy. These components work synergistically to enhance the model's ability to capture both local and global structural dependencies, suppress the influence of anomalous nodes, and assess anomalies from multiple levels of granularity. Anomaly scores are computed by combining reconstruction errors and memory matching signals, resulting in a more robust evaluation. Extensive experiments on seven benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches, offering a comprehensive and generalizable solution for anomaly detection across various graph domains.

Generative learning has advanced network neuroscience, enabling tasks like graph super-resolution, temporal graph prediction, and multimodal brain graph fusion. However, current methods, mainly based on graph neural networks (GNNs), focus solely on structural and topological properties, neglecting cognitive traits. To address this, we introduce the first cognified generative model, CogGNN, which endows GNNs with cognitive capabilities (e.g., visual memory) to generate brain networks that preserve cognitive features. While broadly applicable, we present CogGNN, a specific variant designed to integrate visual input, a key factor in brain functions like pattern recognition and memory recall. As a proof of concept, we use our model to learn connectional brain templates (CBTs), population-level fingerprints from multi-view brain networks. Unlike prior work that overlooks cognitive properties, CogGNN generates CBTs that are both cognitively and structurally meaningful. Our contributions are: (i) a novel cognition-aware generative model with a visual-memory-based loss; (ii) a CBT-learning framework with a co-optimization strategy to yield well-centered, discriminative, cognitively enhanced templates. Extensive experiments show that CogGNN outperforms state-of-the-art methods, establishing a strong foundation for cognitively grounded brain network modeling.

Sentences with multiple quantifiers often lead to interpretive ambiguities, which can vary across languages. This study adopts a cross-linguistic approach to examine how large language models (LLMs) handle quantifier scope interpretation in English and Chinese, using probabilities to assess interpretive likelihood. Human similarity (HS) scores were used to quantify the extent to which LLMs emulate human performance across language groups. Results reveal that most LLMs prefer the surface scope interpretations, aligning with human tendencies, while only some differentiate between English and Chinese in the inverse scope preferences, reflecting human-similar patterns. HS scores highlight variability in LLMs' approximation of human behavior, but their overall potential to align with humans is notable. Differences in model architecture, scale, and particularly models' pre-training data language background, significantly influence how closely LLMs approximate human quantifier scope interpretations.

Sign language production (SLP) aims to translate spoken language sentences into a sequence of pose frames in a sign language, bridging the communication gap and promoting digital inclusion for deaf and hard-of-hearing communities. Existing methods typically rely on gloss, a symbolic representation of sign language words or phrases that serves as an intermediate step in SLP. This limits the flexibility and generalization of SLP, as gloss annotations are often unavailable and language-specific. Therefore, we present a novel diffusion-based generative approach - Text2Sign Diffusion (Text2SignDiff) for gloss-free SLP. Specifically, a gloss-free latent diffusion model is proposed to generate sign language sequences from noisy latent sign codes and spoken text jointly, reducing the potential error accumulation through a non-autoregressive iterative denoising process. We also design a cross-modal signing aligner that learns a shared latent space to bridge visual and textual content in sign and spoken languages. This alignment supports the conditioned diffusion-based process, enabling more accurate and contextually relevant sign language generation without gloss. Extensive experiments on the commonly used PHOENIX14T and How2Sign datasets demonstrate the effectiveness of our method, achieving the state-of-the-art performance.

Large Language Models (LLMs) have demonstrated substantial progress in biomedical and clinical applications, motivating rigorous evaluation of their ability to answer nuanced, evidence-based questions. We curate a multi-source benchmark drawing from Cochrane systematic reviews and clinical guidelines, including structured recommendations from the American Heart Association and narrative guidance used by insurers. Using GPT-4o-mini and GPT-5, we observe consistent performance patterns across sources and clinical domains: accuracy is highest on structured guideline recommendations (90%) and lower on narrative guideline and systematic review questions (60--70%). We also find a strong correlation between accuracy and the citation count of the underlying systematic reviews, where each doubling of citations is associated with roughly a 30% increase in the odds of a correct answer. Models show moderate ability to reason about evidence quality when contextual information is supplied. When we incorporate retrieval-augmented prompting, providing the gold-source abstract raises accuracy on previously incorrect items to 0.79; providing top 3 PubMed abstracts (ranked by semantic relevance) improves accuracy to 0.23, while random abstracts reduce accuracy (0.10, within temperature variation). These effects are mirrored in GPT-4o-mini, underscoring that source clarity and targeted retrieval -- not just model size -- drive performance. Overall, our results highlight both the promise and current limitations of LLMs for evidence-based clinical question answering. Retrieval-augmented prompting emerges as a useful strategy to improve factual accuracy and alignment with source evidence, while stratified evaluation by specialty and question type remains essential to understand current knowledge access and to contextualize model performance.

Although LLM-based conversational agents demonstrate strong fluency and coherence, they still produce undesirable behaviors (errors) that are challenging to prevent from reaching users during deployment. Recent research leverages large language models (LLMs) to detect errors and guide response-generation models toward improvement. However, current LLMs struggle to identify errors not explicitly specified in their instructions, such as those arising from updates to the response-generation model or shifts in user behavior. In this work, we introduce Automated Error Discovery, a framework for detecting and defining errors in conversational AI, and propose SEEED (Soft Clustering Extended Encoder-Based Error Detection), as an encoder-based approach to its implementation. We enhance the Soft Nearest Neighbor Loss by amplifying distance weighting for negative samples and introduce Label-Based Sample Ranking to select highly contrastive examples for better representation learning. SEEED outperforms adapted baselines -- including GPT-4o and Phi-4 -- across multiple error-annotated dialogue datasets, improving the accuracy for detecting unknown errors by up to 8 points and demonstrating strong generalization to unknown intent detection.