Cooperative Coevolution (CC) and Non-Decomposition Algorithms (NDAs) are two primary approaches for large-scale global optimization (LSGO). Although both have achieved success, they exhibit markedly different performance across problem types and each faces approach-specific limitations, leaving the question of "when to...
Wenjie Qiu, Hepei Li, Jianfeng Wang et al.· 0 citations
Analog device sizing must satisfy all specifications across process, voltage, and temperature (PVT) corners to ensure tape-out success, but simulating every candidate at all corners dominates the sizing time and slows design iteration. Conventional surrogate-based optimizers search the sizing space directly, while rece...
Skin cancer is among the most prevalent and life threatening forms of cancer worldwide. Early and accurate diagnosis significantly improves patient survival rates. However, manual dermoscopic assessment remains challenging due to visual similarity. This study proposes an Explainable AI-driven framework for automated bi...
Sanjana Islam Kasfia, Md. Ismiel Hossen Abir, AKM Bahalul Haque et al.· 0 citations
This work proposes \method, an evaluation allocation framework that combines sparse search-time feedback with targeted final confirmation and achieves higher mean performance than full evaluation in most settings while using approximately half as many evaluation cells.
Si-Chun Luo, Yi Huang, Mu-Kai Li et al.· 0 citations
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Safety alignment research for large language models (LLMs) remains concentrated on high-resource languages, leaving open how existing alignment methods perform in low-resource, culturally distinct settings. We study this question for Hausa, a language spoken by more than 200 million people across West Africa. We introd...
Valdini Douglace Lemofouet, Blessing Ngozi Uzor, P. Anyanwu et al.· 0 citations
Multi-relational graph detectors learn from clean data how much to trust each relation, but an adversary pays a different price to manipulate each relation and each action; taking such costs as given, we ask when using a relation is worse than ignoring it. Our first main result proves that degree gating strictly beats...
Long-horizon coding tasks contain nested subtasks. Typically, agents record interactions sequentially into a growing history, causing context inflation and performance degradation. Compressing or trimming the history without explicit subtask boundaries can discard information needed by unfinished subtasks. Conversely,...
Jiahong Xiang, Kunqiu Chen, Lingming Zhang et al.· 0 citations
Reinforcement learning for long-horizon agents relies on feedback from sequences of interactions, making credit assignment to individual actions difficult. Existing methods such as GiGPO compare actions at shared states to compute relative advantages, but estimate each action's value from a single sampled continuation....
Yingjian Chen, Lang Zhou, Zixin Zhao et al.· 0 citations
AI research agents can propose, execute, and improve programs that solve machine learning tasks. However, executing each candidate program requires training and evaluating ML models, limiting how many candidates an agent can explore at each step. We introduce MLE-World, a world model finetuned from Qwen3.5-4B that pred...
Brian Ko, Han Wang, Hejia Geng et al.· 0 citations
Open-vocabulary 3D occupancy prediction represents scene geometry and semantics in a unified voxel grid, enabling recognition beyond predefined categories. One approach reconstructs scenes with 3D Gaussians and converts them into voxel occupancy. However, Gaussian scale inflation and excessive overlap can distort voxel...
Bohua Wang, Congyang Zhao, Tianxiong Lv et al.· 0 citations
In replay-free continual learning, Elastic Weight Consolidation (EWC) uses Fisher information matrix (FIM) to protect acquired knowledge by penalizing changes to historically important parameters. However, a local importance estimate at the retained solution does not in general characterize sensitivity after finite par...
Liying Wang, Simone Magistri, Joost van de Weijer· 0 citations
