arXiv:2607.01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a…
arXiv:2607.01083v2 Announce Type: replace-cross Abstract: High-throughput RLHF systems often decouple rollout generation from policy optimization, leading to the use of…
arXiv:2607.01984v1 Announce Type: cross Abstract: Newer lightweight convolutional neural networks are often presented as improving predictive performance and deployment efficiency,…
arXiv:2607.01982v1 Announce Type: cross Abstract: Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and…
arXiv:2607.00836v2 Announce Type: replace-cross Abstract: World models are increasingly used in embodied intelligence and generative simulation, yet their scope remains…
Nature Machine Intelligence, Published online: 03 July 2026; doi:10.1038/s42256-026-01267-z Berner et al. show how to adapt popular neural networks into…
arXiv:2607.00001v1 Announce Type: new Abstract: Most approaches to AI alignment treat human preferences as fixed targets to be inferred and…
arXiv:2606.31522v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral…
