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arXiv:2608.14550v1 Announce Type: new Abstract: AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs.
arXiv:2608.14552v1 Announce Type: new Abstract: Large language models LLMs are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty.
arXiv:2608.14558v1 Announce Type: new Abstract: Current multimodal models have demonstrated remarkable proficiency in recognizing static visual and auditory content.
arXiv:2608.14559v1 Announce Type: new Abstract: Effective communication in multiagent reinforcement learning requires agents to decide not only \textit{what} to communicate, but when?
arXiv:2608.14562v1 Announce Type: new Abstract: AI governance is shifting from voluntary ethics to enforceable, riskbased regulation, yet crossjurisdictional divergence creates compliance uncertainty for operators of highstakes AI.
arXiv:2608.14565v1 Announce Type: new Abstract: AI safety research has mainly focused on two areas: technical alignment ensuring AI systems produce humanaligned outputs and the regulation of generative AI's societal impacts including unemployment risk and labor market disruption.
arXiv:2608.14566v1 Announce Type: new Abstract: Recent work on evaluating the moral competence of large language models LLMs has focused primarily on what we call the moral value problem, i.e., whether model outputs align with human moral values. In contrast, the moral norm problem, i.e.
arXiv:2608.14567v1 Announce Type: new Abstract: This paper presents a targeted narrative review establishing the historical and theoretical foundations for computational belief change implementation.
arXiv:2608.14568v1 Announce Type: new Abstract: As Artificial Intelligence AI systems become deeply integrated into critical global infrastructure, the urgency for robust governance frameworks has intensified.
arXiv:2608.14569v1 Announce Type: new Abstract: Neural solvers for constraint satisfaction problems have achieved remarkable indistribution accuracy, yet they suffer from a fundamental limitation persistent constraint violations occur under distribution shifts even when the model reports high...
Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
NVIDIA teams use ChatGPT Work to reduce manual tasks, connect fastmoving signals, and scale successful workflows globally.