arXiv (All)
open access
Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations
Retail supply chain operations rely on coupled decision modules that must adapt as requirements evolve. LLMs offer a natural-language interface for this task, but existing methods primarily focus on individual optimization models. Extending them to heterogeneous decision pipelines is challenging bec
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Autonomous Assessment of Generalizability of AI Agent Capabilities#1110150
- Exploring Autonomous Agentic Data Engineering for Model Specialization#800049
- UniACE: A Unified Framework for Evaluating LLM Agentic Capabilities#820473
- Evolving Excellence: Automated Optimization of LLM-based Agents#925873
- SWE-Serve: Benchmarking Agentic Engineering For Production Inference Serving#1062702
Record · ID 926737
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive