The paper presents a graph‑constrained agentic framework that enables large language models to adapt retail supply‑chain decision modules to evolving requirements. It jointly selects intervention routes and admissible module changes, validating candidates against downstream KPIs. Experiments with 100 warehouse requirements and three LLMs show the framework improves end‑to‑end success from 72–76% to 79–83%.
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...
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.
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.
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%.
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.
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.
arXiv:2609.01045v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...
arXiv:2605. 07699v2 Announce Type: replace-cross Abstract: LLM-based agents are increasingly deployed for routine but consequential tasks in real-world domains, where their behavior is governed by inherently ambiguous domain policies that admit multiple valid interpretations.
Agent4RE is a self‑refining multi‑agent framework designed for end‑to‑end software requirements engineering (RE). It coordinates specialized agents and includes two iterative improvement loops, and introduces a new real‑world dataset, RE‑E2E, for evaluating RE workflows from elicitation to generation. The framework, along with two enhanced variants that use autonomous self‑refinement or structured human feedback, consistently outperforms baseline prompting methods across eight large language models, achieving higher text‑based metrics and superior human and LLM‑as‑a‑judge ratings.
arXiv:2601.11100v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are reshaping the industrial landscape. However, most practical agents still rely on human-designed scaffolds, ma...
The paper introduces GEARS, a framework that treats ranking optimization as an autonomous discovery process within a programmable experimentation environment. By encapsulating ranking expert knowledge into reusable agent skills, GEARS allows operators to steer systems through high-level intent personalization rather than static model selection. The framework also includes validation hooks to enforce statistical robustness and filter out brittle policies, and experimental results show that GEARS consistently finds near‑Pareto‑efficient policies while maintaining deployment stability.
arXiv:2607. 10768v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed to solve optimization problems, yet existing benchmarks evaluate them on pre-structured mathematical formulations that bypass the most critical challenge: translating complex business requirements into correct models and solve efficiently.