Alignment, bias, regulation, and responsible AI
A Reuters report outlines China's proposed regulations on the rapidly expanding sector of digital humans and AI avatars. Under the new dr...
![[2512.00408] Low-Bitrate Video Compression through Semantic-Conditioned Diffusion](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2512.00408: Low-Bitrate Video Compression through Semantic-Conditioned Diffusion
Abstract page for arXiv paper 2510.15148: XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models
This paper presents CMAFNet, a novel network for detecting small defects in transmission lines using RGB-D data, achieving significant pe...
This paper presents a unified framework for Query Auto-Completion (QAC) that integrates Retrieval-Augmented Generation (RAG) and multi-ob...
This paper presents a framework for interpreting emergent extreme events in multi-agent systems, focusing on the origins and drivers of t...
The paper introduces RCGP-UCB, a robust Bayesian optimization algorithm designed to handle extreme outliers by allowing unbounded corrupt...
This article presents a framework-agnostic, agent-based operationalization of the DISARM framework to investigate Foreign Information Man...
This paper investigates the high-dimensional asymptotics of differentially private PCA, focusing on optimal noise levels for privacy guar...
This article explores the detection of 19 human values in sentences using transformer models, demonstrating the learnability of moral pre...
This paper presents a method for bias-corrected data synthesis aimed at improving classification accuracy in imbalanced learning scenario...
This article presents a framework for early fault diagnosis and self-recovery in strawberry harvesting robots, leveraging vision-based te...
The paper introduces IntentMiner, a novel approach to detect Intent Inversion Attacks in Large Language Models (LLMs) by analyzing tool c...
The paper presents the ALERT dataset and an input-size-agnostic Vision Transformer (ISA-ViT) for driver activity recognition using IR-UWB...
This paper presents a framework for formal reasoning about the confidence and robustness of neural networks, proposing a unified techniqu...
The paper presents AthenaBench, a dynamic benchmark designed to evaluate large language models (LLMs) in the context of Cyber Threat Inte...
This article explores the trade-offs between fairness and accuracy in predictive modeling, introducing the fairness-accuracy (FA) Pareto ...
The paper presents Recall, a novel adversarial framework that targets the robustness of image generation model unlearning, revealing vuln...
The paper presents Lorica, a novel framework aimed at enhancing personalized adversarial robustness in machine learning models, particula...
The paper presents SECA, a method for eliciting hallucinations in large language models (LLMs) through semantically equivalent and cohere...
This article presents EAPrivacy, a benchmark for evaluating the physical-world privacy awareness of large language models (LLMs), reveali...
The paper introduces BiasFreeBench, a benchmark designed to evaluate bias mitigation techniques in large language models (LLMs) by provid...
The paper introduces EVALOOOP, a framework for assessing the robustness of large language models (LLMs) in programming tasks through self...
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