arXiv:2609.13561v1 Announce Type: new
Abstract: Efficient utilization of supply chain analytics for decision making remains a significant challenge for planners, as critical tasks such as database qu...
By Xian Yeow Lee, Teppei Inoue, Haiyan Wang, Chetan Gupta
arXiv:2608. 15591v1 Announce Type: new Abstract: Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve.
By Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge, Ashmita Kapoor, Tanya Dixit
arXiv:2606. 26852v1 Announce Type: new Abstract: Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing.
By Janik Bischoff, Anne Meyer, Uta Mohring, Fabian Dunke, Maximilian Barlang, \"Ozge Nur Subas, Hadi Kutabi, Stefan Nickel, Kai Furmans
The paper introduces ARC, a lightweight hierarchical policy that learns to configure LLM‑based agent systems on a per‑query basis by treating each configuration as a temporally extended option in a semi‑Markov decision process. Unlike fixed templates or hand‑tuned heuristics, ARC dynamically selects workflows, tools, token budgets, and prompts tailored to the difficulty of each query. Experiments on reasoning, tool‑use, and agentic benchmarks show that ARC outperforms budget‑matched tool‑augmented LLMs, boosting reasoning accuracy by 31.3%, tool‑use accuracy by 13.95%, and doubling success on the τ‑Bench Airline Pass task from 9.0% to 18.0%.
By Aditya Taparia, Som Sagar, Ransalu Senanayake
arXiv:2607. 17331v1 Announce Type: new Abstract: Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries.
By Zhihao Liu, Tianyu Wang, Xi Vincent Wang, Lihui Wang
arXiv:2607. 03651v1 Announce Type: new Abstract: While traditional hub capacity planning models optimize effectively for quantitative inputs, they often fail to digest qualitative business context.
By Xiaoyue Liu, Zheng Dong