arXiv:2606. 15115v1 Announce Type: new Abstract: Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives.
By Yiyi Zhu, Yaolin Wen, Xiang Xia, Xin An, Hanyi Si, Xiang Shu, Yangde Fu, Liang Dou, Hong Qian
arXiv:2602. 07764v2 Announce Type: replace-cross Abstract: Multi-objective reinforcement learning (MORL) seeks to train agents capable of balancing conflicting objectives.
By Tanmay Ambadkar, Sourav Panda, Shreyash Kale, Jonathan Dodge, Abhinav Verma
arXiv:2606. 26397v1 Announce Type: cross Abstract: Real-world decision-making often requires balancing multiple conflicting objectives, a challenge that standard Reinforcement Learning (RL) frequently addresses by aggregating rewards into a single scalar signal.
By Aniruddha Joshi, Niklas Lauffer, Sanjit Seshia
arXiv:2606. 03904v1 Announce Type: new Abstract: Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost universally hand their reconciled directions to Adam~\cite{kingma2015adam}.
By Fengbei Liu, Rachit Saluja, Sunwoo Kwak, Ruibo Wang, Ruining Deng, Heejong Kim, Johannes C. Paetzold, Mert R. Sabuncu
arXiv:2605. 01712v2 Announce Type: replace Abstract: Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optimal solutions.
By Xinyue Chen, Yingxuan Liang, Yiqin Huang, Chikai Shang, Hai-Lin Liu, Fangqing Gu
arXiv:2606. 24042v1 Announce Type: new Abstract: Recommender systems often induce filter bubbles and semantic homogenization by monolithically optimizing for immediate user engagement.
By Cl\'audio L\'ucio Do Val Lopes, Lucca Machado da Silva, Andr\'e de Oliveira Brand\~ao