The paper introduces a history‑aware offline reinforcement learning policy that predicts iterative cost weights for routing in dense integrated circuit designs. By incorporating a lightweight LSTM and additional router features, the policy retains sequence context and improves convergence across various placement densities and guide qualities. Integrated into any cost‑based router with minimal changes, the approach reduces design rule violations by an average of 92% and cuts runtime by 10%.
arXiv:2606. 00395v1 Announce Type: cross Abstract: Mixture of Experts (MoE) Large Language Models (LLMs) achieve strong performance at scale.
By Daize Dong, Junlin Chen, Haolong Jia, Jiawei Wu, Huanwei Di, Jiang Liu, Jialian Wu, Zhengzhong Liu, Zicheng Liu, Emad Barsoum, Dimitris N. Metaxas, Hongyi Wang
arXiv:2609.08189v1 Announce Type: new
Abstract: Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, ho...
By Hongjin Lin, Wentao Wan, Keze Wang
The paper introduces MEMENTO, a memory‑enhanced neural solver that improves routing problem solutions by using online data from repeated attempts to adjust action distributions during inference. It targets NP‑hard routing tasks such as the Traveling Salesman and Capacitated Vehicle Routing problems, outperforming existing tree‑search and policy‑gradient fine‑tuning methods. MEMENTO demonstrates strong scalability and data efficiency, achieving state‑of‑the‑art results on 11 of 12 evaluated tasks and enabling zero‑shot integration with diversity‑based solvers.
By Felix Chalumeau, Refiloe Shabe, Noah De Nicola, Arnu Pretorius, Thomas D. Barrett, Nathan Grinsztajn
arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.
By Mohsen Dastpak, Fausto Errico, Ola Jabali
arXiv:2607. 22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI.
By Ritik Raj, Souvik Kundu, Sarbartha Banerjee, Dheemanth Joshi, Ishita Vohra, Tushar Krishna
arXiv:2609.23085v1 Announce Type: cross
Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajector...
By Muhammad Abdur Rab Siddiqui, Daniela Rojas, Chen Yang, Wenqi Cui, Yuanyuan Shi, Yize Chen
arXiv:2608. 12146v1 Announce Type: cross Abstract: Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks.
By Yibo Shen, Xudong Han, Xiaowei Zhu, Gen Li, Zhenxuan Pan
arXiv:2609.13058v1 Announce Type: new
Abstract: Reinforcement learning (RL) has become central to post-training of large language models. Recent advances in RL for Mixture-of-Experts (MoE) models hav...
By Hongyi He, Zhenghao Lin, Xiao Liu, Peng Cheng, Yan Lu, Yeyun Gong
arXiv:2608.30512v1 Announce Type: cross
Abstract: Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersecti...
By Cheng Gu, Qiusheng Zhao, Anbang Liu, Shaochong Lin, Max Z. J. Shen
arXiv:2608. 05872v1 Announce Type: cross Abstract: Standard Large Language Models (LLMs) execute layers sequentially.
By Pawe{\l} Batorski, Abtin Pourhadi, Akylgali Aitaza, Przemys{\l}aw Spurek, Paul Swoboda
arXiv:2607. 22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.
By Yida Xu, Zhaofang Mao, Yuheng Miao, Jiaxin Zhang, Yiting Sun