11696 papers
This paper presents YAQIN, a co-designed AI application that integrates Islamic frameworks and user-centered design to provide culturally sensitive mental health support for Muslim women in the UK, addressing gaps in trust and engagement through a faith-aware chatbot and guided journaling tool.
This paper demonstrates that large language models exhibit robust decision biases in two-arm bandit tasks, characterized by stubborn exploitation and low learning rates that persist across decoding parameters, thereby posing significant challenges for optimal human-AI collaboration.
This paper proposes a novel multi-agent architecture that automates structural modeling and analysis using OpenSeesPy by decomposing complex tasks into specialized agents to effectively reduce hallucinations and error accumulation, achieving high accuracy and scalability across benchmark frame problems.
This paper evaluates the capabilities of various large language models, including Llama-3 and ChatGPT, in solving diverse discrete optimization problems using natural language datasets, revealing that while stronger models generally perform better, Chain-of-Thought reasoning is not universally effective and data augmentation can improve performance on simpler tasks despite introducing instability.
The paper proposes FedShift, a novel two-stage "Hide and Find" distributed adversarial attack for Federated Graph Learning that injects hidden shifters to stealthily guide poisoned data toward a target boundary and efficiently generates perturbations, achieving superior effectiveness, robustness against defenses, and a 90% reduction in convergence time compared to existing methods.
The paper proposes DECADE, an unsupervised diffusion model that achieves temporally consistent denoising of Rb-82 dynamic cardiac PET images without paired training data, effectively reducing noise while preserving quantitative accuracy for myocardial blood flow and flow reserve metrics.
The QuadAI system for SemEval-2026 Task 3 achieves superior performance in dimensional aspect-based sentiment regression by employing an ensemble learning framework that combines a hybrid RoBERTa encoder with large language models, leveraging the complementary strengths of both architectures to significantly reduce RMSE and improve correlation scores.
ProgAgent is a JAX-native continual reinforcement learning agent that mitigates catastrophic forgetting and reward specification costs by unifying progress-aware reward learning from unlabeled videos with adversarial refinement and a stability-plasticity balanced architecture, achieving superior performance on robotic manipulation benchmarks.
This study evaluates seven state-of-the-art large language models in the underrepresented Nepali cultural context using a Dual-Metric Bias Assessment framework, revealing that while explicit agreement with biased statements is measurable, implicit generative bias is distinct, follows a non-linear relationship with temperature, and is poorly predicted by agreement metrics, thereby highlighting the critical need for culturally grounded datasets and evaluation strategies.
This paper proposes a Physics Informed Neural Network framework with multi-head linear layers and orthogonality constraints to construct robust, interpretable low-dimensional embeddings for the viscous Burgers' equation, demonstrating that a small number of latent modes effectively capture the solution space's dominant dynamics.
HybridStitch is a novel Text-to-Image generation paradigm that accelerates diffusion models by treating generation as an editing process, dynamically splitting the image into easy and complex regions to leverage a small model for coarse sketching and a large model for targeted refinement, thereby achieving a 1.83× speedup on Stable Diffusion 3.
This paper generalizes the Micro-Transit Zoning Problem to incorporate a global budget and proposes an efficient Column Generation framework with pricing heuristics that outperforms existing enumeration-based approaches in solution quality and scalability across major U.S. cities.
This paper introduces Gradient Iterated Temporal-Difference (GTD) learning, a novel algorithm that modifies iterated TD by computing gradients over moving targets to achieve the stability of gradient methods while matching the competitive learning speed of semi-gradient methods across diverse benchmarks like Atari games.
This paper argues that addressing AI misuse in education requires shifting from unreliable detection methods to a "Learning Visibility Framework" that treats the learning process as assessable evidence, thereby fostering ethical AI integration through transparency and shared understanding rather than surveillance.
The paper introduces DistillGuard, a framework that systematically evaluates output-level defenses against LLM knowledge distillation and finds that most current approaches are largely ineffective, with performance degradation being highly task-dependent and insufficient to broadly prevent knowledge theft.
The paper introduces AI Steerability 360, an open-source, Hugging Face-native Python toolkit that provides a unified interface for composing, evaluating, and comparing diverse large language model steering methods across input, structural, state, and output control surfaces.
This paper presents a systematic study demonstrating that LLM agents can be engineered to intentionally deceive other agents in multi-agent systems by inferring their motivations and employing strategic misdirection rather than fabrication, revealing that current fact-checking defenses are insufficient against such targeted attacks.
The paper introduces SynPlanResearch-R1, a framework that synthesizes tool-use trajectories to encourage deeper exploration during supervised fine-tuning, thereby overcoming the limitations of reinforcement learning with verifiable rewards and significantly improving research agent performance across multiple benchmarks.
Inspired by biological sleep, the paper introduces Sleep Replay Consolidation (SRC), a post-training method that selectively replays internal representations to improve artificial neural network calibration and trustworthiness without requiring supervised retraining.
This paper introduces a formal framework for "informativeness" and a corresponding hospitality-specific VQA dataset to evaluate Vision-Language Models, revealing that while current models struggle with decision-oriented reasoning, their performance significantly improves with modest domain-specific finetuning.