Search: “safety”

arXiv:2605.25984v1 Announce Type: cross Abstract: Ensuring safe and contextually appropriate behaviour in Large Language Models (LLMs) remains a critical challenge for real-world deployment. We presen

arXiv:2603.22348v3 Announce Type: replace Abstract: Online learning algorithms often face a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing

arXiv:2605.23157v1 Announce Type: new Abstract: The attack surface of a multimodal large language model (MLLM) is language-dependent in ways that reveal the mechanistic structure of alignment failures

arXiv:2605.22001v1 Announce Type: cross Abstract: Injection detectors deployed to protect LLM agents are calibrated on static, template-based payloads that announce themselves as override directives.

arXiv:2605.20469v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used for medical image interpretation, yet they frequently hallucinate, generating clinically plausible b

arXiv:2605.20296v1 Announce Type: new Abstract: Fine-tuning a language model for a target task routinely degrades capabilities the training data never explicitly threatened. We study this phenomenon,

arXiv:2605.18915v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are vulnerable to jailbreak attacks, which can elicit harmful responses from MLLMs. Many MLLMs support multi-

arXiv:2605.18309v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) achieve strong alignment through supervised fine-tuning and reinforcement learning from human feedback, the alig

arXiv:2605.16707v1 Announce Type: cross Abstract: The rapid evolution of digital health technologies is redefining healthcare services worldwide. The integration of wireless communication and Internet

arXiv:2510.07239v2 Announce Type: replace Abstract: Automated red-teaming has emerged as a scalable approach for auditing Large Language Models (LLMs) prior to deployment, yet existing approaches lack

arXiv:2605.15377v1 Announce Type: new Abstract: As AI systems are increasingly deployed in autonomous agentic settings at scale, it is important to ensure the actions they take are safe and aligned wi

arXiv:2506.08584v4 Announce Type: replace Abstract: Medical question answering (QA) benchmarks often focus on multiple-choice or fact-based tasks, leaving open-ended answers to real patient questions

arXiv:2605.13542v1 Announce Type: new Abstract: Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states u

arXiv:2605.11533v1 Announce Type: new Abstract: Clinical check-up reports are multimodal documents that combine page layouts, tables, numerical biomarkers, abnormality flags, imaging findings, and dom

arXiv:2603.28561v2 Announce Type: replace Abstract: The growing deployment of small Unmanned Aerial Systems (sUASs) in low-altitude airspaces has increased the need for reliable tactical deconfliction

arXiv:2601.01762v2 Announce Type: replace-cross Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes. W

arXiv:2605.10067v1 Announce Type: cross Abstract: Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing ap

arXiv:2605.03476v1 Announce Type: new Abstract: Discharge summaries require extracting critical information from lengthy electronic health records (EHRs), a process that is labor-intensive when perfor

arXiv:2605.02398v1 Announce Type: cross Abstract: As frontier AI models are deployed in high-stakes decision pipelines, their ability to maintain metacognitive stability -- knowing what they do not kn

arXiv:2605.00005v1 Announce Type: new Abstract: The increasing deployment of deep neural networks (DNNs) in cyber-physical systems (CPS) enhances perception fidelity, but imposes substantial computati

arXiv:2604.26973v1 Announce Type: cross Abstract: Multiobjective optimization remains challenging for many scientific and engineering problems due to the need to balance convergence, diversity, and co

arXiv:2508.04325v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show significant potential in healthcare, prompting numerous benchmarks to evaluate their capabilities. However,

arXiv:2604.24086v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have been demonstrated possessing strong zero-shot generalization for robot control, their massive parameter

arXiv:2604.23829v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) extract millions of interpretable features from a language model, but flat feature inventories aren't very useful on their ow

arXiv:2604.24542v1 Announce Type: cross Abstract: Large language models deployed at runtime can misbehave in ways that clean-data validation cannot anticipate: training-time backdoors lie dormant unti

arXiv:2604.23460v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has emerged as a key technique for eliciting complex reasoning in Large Language Models (LLMs). Although interpretable,

arXiv:2604.22136v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly issue API calls that mutate real systems, yet many current architectures pass stochastic model outputs

arXiv:2511.22277v2 Announce Type: replace Abstract: Large language models (LLMs) have shown remarkable ability to generate code, yet their outputs often violate syntactic or semantic constraints when

arXiv:2603.27112v2 Announce Type: replace Abstract: As Automatic Train Operation (ATO) advances toward GoA4 and beyond, it increasingly depends on efficient, reliable cab-view visual perception and de

arXiv:2604.20496v1 Announce Type: cross Abstract: The April 2026 Claude Mythos sandbox escape exposed a critical weakness in frontier AI containment: the infrastructure surrounding advanced models rem

arXiv:2509.15174v3 Announce Type: replace-cross Abstract: WARNING: This paper contains examples of offensive materials. To address the proliferation of toxic content on social media, we introduce SMAR

arXiv:2604.19841v1 Announce Type: cross Abstract: Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to

arXiv:2604.19598v1 Announce Type: cross Abstract: This study compared repeated generation consistency of exercise prescription outputs across three large language models (LLMs), specifically GPT-4.1,

arXiv:2604.19480v1 Announce Type: new Abstract: While foundation models drive steady progress in image segmentation and diffusion algorithms compose always more realistic images, the seemingly simple

arXiv:2604.19124v1 Announce Type: new Abstract: Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity

arXiv:2604.19274v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as co-authors in collaborative writing, where users begin with rough drafts and rely on LLMs to compl

arXiv:2604.19298v1 Announce Type: cross Abstract: We introduce IndiaFinBench, to our knowledge the first publicly available evaluation benchmark for assessing large language model (LLM) performance on

arXiv:2512.09427v5 Announce Type: replace-cross Abstract: Existing memory management techniques severely hinder efficient Large Language Model serving on accelerators constrained by poor random-access

arXiv:2508.14913v4 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated significant capabilities in solving mathematical problems expressed in natural language. However, mul

arXiv:2604.16582v1 Announce Type: new Abstract: Pedestrian detection is fundamental to autonomous driving, robotics, and surveillance. Despite progress in deep learning, reliable identification remain

arXiv:2604.16886v1 Announce Type: new Abstract: Generalist embodied agents must perform interactive, causally-dependent reasoning, continually interacting with the environment, acquiring information,

arXiv:2510.11288v4 Announce Type: replace Abstract: Recent work has shown that narrow finetuning can produce broadly misaligned LLMs, a phenomenon termed emergent misalignment (EM). While concerning,

arXiv:2604.16405v1 Announce Type: cross Abstract: Video-generative world models are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to predict physi

arXiv:2604.17736v1 Announce Type: new Abstract: As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive gener

arXiv:2604.17282v1 Announce Type: new Abstract: Process-Level Reward Models (PRMs) are essential for guiding complex reasoning in large language models, yet existing PRM benchmarks cover only general

arXiv:2510.16054v2 Announce Type: replace-cross Abstract: When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send th