arXiv:2606. 29366v1 Announce Type: cross Abstract: Balance-oriented multi-warehouse inventory allocation is a recurring decision problem in large-scale e-commerce supply chains, in which a fixed replenishment quantity is distributed across warehouses to balance post-allocation inventory coverage while accounting for demand forecasts and heterogeneous allocation constraints.
By Jintao Xu, Yingzheng Ma, Jiong Dong, Yongzhi Qi, Jianshen Zhang, Dongyang Geng, Anni Zhang
Balance-oriented multi-warehouse inventory allocation is a recurring decision problem in large-scale e-commerce supply chains, in which a fixed replenishment quantity is distributed across warehouses to balance post-allocation inventory coverage while accounting for demand forecasts and heterogeneous allocation constraints. In practice, allocation requirements are often scenario-dependent and expressed in semi-structured or natural-language form rather than as ready-to-solve operations research (OR) formulations.
arXiv:2609.08071v1 Announce Type: new
Abstract: Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize th...
By Fenghua Yang, Preet Baxi, Yi Zhang, Stefanus Jasin, Yanzhe Lei, Mo Liu, Parshan Pakiman
arXiv:2510. 08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, where semantic understanding and complex reasoning directly shape the user experience and business conversion.
By Jianhui Yang, Yiming Jin, Pengkun Jiao, Chenhe Dong, Zerui Huang, Shaowei Yao, Xiaojiang Zhou, Dan Ou, Haihong Tang
The paper investigates whether large language models (LLMs) can design effective algorithms for well-specified operations research (OR) problems, focusing on inventory control, queueing network control, and assortment optimization. Two usage levels are examined: (1) the model receives a single problem instance and outputs a solution, and (2) the model receives only a problem class description and returns a general algorithm mapping instance parameters to solutions. Using a single untuned prompt and a Python sandbox, the strongest tested model, gpt-5.6-sol, matches or surpasses existing methods on nearly all evaluated instances, even when the algorithm is fixed before seeing evaluation cases, and performance improves markedly across models released within eight months.
By Jackie Baek
Compass‑v3 is a 245B‑parameter Mixture‑of‑Experts language model tailored for Southeast Asian e‑commerce, featuring 71B active parameters per token and hardware‑efficient expert parallelism. It is trained on 12 trillion multilingual tokens and synthetic e‑commerce instructions, and incorporates Optimal‑Transport Direct Preference Optimization to improve instruction adherence. Benchmarks show it outperforms GPT‑4, DeepSeek‑V3.1, and Qwen3‑235B, and it is already deployed at scale on Shopee, handling over 70% of the platform’s LLM traffic.
By Sophia Maria
arXiv:2608. 02612v1 Announce Type: cross Abstract: Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise.
By Yutaro Yamada, Kei Hiroshima, Nozomu Yoshinari, Kento Uchida, Shinichi Shirakawa
arXiv:2608. 19993v1 Announce Type: new Abstract: Loading reusable skill documents into a bounded context window is now the primary way large language model (LLM) agents acquire task-specific capabilities, which makes skill selection a first-order determinant of task performance and token cost.
By Yu Chen, Ruishuo Chen, Xun Wang, Zhuoran Li, Longbo Huang
arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.
By Yuecheng Li, Zeyu Song, Jing Yao, Chi Lu, Peng Jiang, Kun Gai
arXiv:2607. 00011v2 Announce Type: replace-cross Abstract: Reusable agent skills are emerging as a service-oriented capability layer for Large Language Model (LLM) agents.
By Jingyuan Zheng, Dongjing Wang, Xin Zhang, Hao Chen, Youhuizi Li, Xudong Shen, Haiping Zhang, Butian Huang, Dongjin Yu, Guandong Xu
The paper introduces OptTips, a knowledge base of 50 expert optimization modeling techniques, and OptDachshund, a multi‑agent framework that generates mathematical models and solver code from natural‑language problem descriptions. Using these tools, the authors create the EfficientOpt benchmark, comprising 561 expert‑reviewed tasks with paired reference implementations, to evaluate large language models (LLMs) on both correctness and computational efficiency. Their experiments with 11 LLMs show a consistent efficiency gap: even when LLMs produce correct solutions, the resulting programs often take longer to solve than expert‑crafted counterparts, highlighting the need to assess both accuracy and runtime performance in LLM‑based optimization modeling.
By Zhong Li, Xin Huang, Jinhui Wan, Xiangyi Wang, Shenkai Zhang, Ruiqi Chen, Wenyu Liu, Zaiwen Wen, Ziyan Luo
arXiv:2607. 25675v1 Announce Type: new Abstract: Text-space optimization adapts large language models (LLMs) by editing external natural-language artifacts rather than model weights, so the optimized artifacts remain inspectable and the model can be treated as a black box.
By Jiangwang Chen, Zixin Song, Junlin Liu, Shuaiyu Zhou, Haiyan Wu, Haihan Shi, Chenxi Zhou, Hanqing Li, Xiao Yang, Da Zhu, Guanjun Jiang, Hai Wan, Xibin Zhao