17 accepted papers
Modern manufacturing systems demand real-time, trustworthy, and interpretable insights into anomalies and their underlying causes. However, conventional pipelines treat anomaly detection, causal inference, and decision-making as siloed tasks, lacking integration, explainability, and adaptability. We
Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI systems often function as isolated black boxes, lacking the seamless integration of prediction, explanation, and causal r
LLMs are increasingly being deployed as chatbots, but today’s interfaces offer little to no friction: users interact through seamless conversations that conceal when the model is drifting, hallucinating or failing. This lack of transparency fosters blind trust, even as models produce unstable or rep
Autoregressive language models frequently degrade during long-horizon generation, producing repetitive text, losing instruction adherence, and exhibiting unstable entropy. Despite the prevalence of these failures, practitioners lack online diagnostics to detect them in real time as they occur. We fo…
Alignment is crucial for text-to-image (T2I) models to ensure that the generated images faithfully capture user intent while maintaining safety and fairness. Direct Preference Optimization (DPO) has emerged as a key alignment technique for large language models (LLMs), and its influence is now exten
Retrieval-Augmented Generation (RAG) has become the standard approach for integrating domain knowledge into Large Language Models (LLMs). However, fair comparison of RAG pipelines remains difficult: data preparation is often ad hoc, subsampling methods are opaque, parameters vary across implementati
AI-driven education platforms have made some progress in personalisation, yet most remain constrained to static adaptation—predefined quizzes, uniform pacing, or generic feedback—limiting their ability to respond to learners’ evolving understanding. This shortfall highlights the need for systems tha
Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, completing analogies like “Oxygen is to Gas as < blank > is to < blank >" requires identifying the semantic relationship (e.g.…
In modern assembly pipelines, identifying anomalies is crucial in ensuring product quality and operational efficiency. Conventional single-modality methods fail to capture the intricate relationships required for precise anomaly prediction in complex predictive environments with abundant data and mu
Precise alignment in Text-to-Image (T2I) systems is crucial for generating visuals that reflect user intent while adhering to ethical and policy standards. Recent controversies, such as the Google Gemini-generated Pope image backlash, highlight the urgent need for robust alignment mechanisms. Buildi…
This paper addresses the time-intensive nature of systematic reviews (SRs) and proposes a solution leveraging advancements in Generative AI (e.g., ChatGPT) and external knowledge augmentation (e.g., Retrieval-Augmented Generation). The proposed system, GEAR-Up, automates query development and transl…
Over the past decade, analogies, in the form of word-level analogies, have played a significant role as an intrinsic measure of evaluating the quality of word embedding methods such as word2vec. Modern large language models (LLMs), however, are primarily evaluated on extrinsic measures based on benc…
Generating high-quality annotations for object detection and recognition is a challenging and important task, especially in relation to safety-critical applications such as autonomous driving (AD). Due to the difficulty of perception in challenging situations such as occlusion, degraded weather, and…
After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically vali…
Automatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fact-checker generally follows several logical steps to verify a verisimilitude cla…
Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, health-care workers require practical and scalable depressive disorder screening and triage system. This study aims to…
One of the cardinal tasks in achieving robust medical question answering systems is textual entailment. The existing approaches make use of an ensemble of pre-trained language models or data augmentation, often to clock higher numbers on the validation metrics. However, two major shortcomings impede…