12 papers
Quantum Incremental Learning with Mixed State Prototypes
Yu Wu, Qianli Zhou, Xinyang Deng +3
Incremental learning models are required to learn new classes sequentially without catastrophic forgetting, while operating under parameter and memory constraints. In the Noisy Int…
FibVLA: An Efficient Temporal Vision-Language-Action Model with Fibonacci Sampling
Li Lin, Wujun Xu, Weiwei Meng +3
Vision-language-action models (VLAs), which leverage the cognition of multimodal information to infer physical-world actions, provide a generalized solution for embodied AI applica…
Stochastic Multipath Routing for High-Throughput Entanglement Distribution in Quantum Repeater Networks
Ankit Mishra, Kang Hao Cheong
Quantum repeater networks distribute entanglement over lossy links while many users share a limited pool of entangled pairs. Most existing routing schemes either always use a singl…
Structure-Aware Cooperative Ensemble Evolutionary Optimization on Combinatorial Problems with Multimodal Large Language Models
Jie Zhao, Kang Hao Cheong
Evolutionary algorithms (EAs) have proven effective in exploring the vast solution spaces typical of graph-structured combinatorial problems. However, traditional encoding schemes,…
Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems?
Jie Zhao, Tao Wen, Kang Hao Cheong
Large Language Models (LLMs) have shown strong capabilities in language understanding and reasoning across diverse domains. Recently, there has been increasing interest in utilizin…
Multidomain Evolutionary Optimization on Combinatorial Problems in Complex Networks
Jie Zhao, Kang Hao Cheong, Yaochu Jin
Knowledge transfer-based evolutionary optimization has garnered significant attention, such as in multi-task evolutionary optimization (MTEO), which aims to solve complex problems…