arXiv AI By Yash Pulse, Yong-Bin Kang, Abhik Banerjee, Prem Prakash Jayaraman

FacProcessTwin: An LLM-Based System for Process Twin Development

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arXiv:2606. 17666v1 Announce Type: cross Abstract: Process twins provide real-time representations of entire production processes.

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arXiv AI
Aug 3

Compiled AI: Deterministic Code Generation for LLM-Based Workflow Automation

arXiv:2604. 05150v2 Announce Type: replace-cross Abstract: We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which workflows execute deterministically without further model invocation.

By Geert Trooskens (XY.AI Labs, Palo Alto, CA), Aaron Karlsberg (XY.AI Labs, Palo Alto, CA), Anmol Sharma (XY.AI Labs, Palo Alto, CA), Lamara De Brouwer (XY.AI Labs, Palo Alto, CA), Max Van Puyvelde (Stanford University School of Medicine, Stanford, CA), Matthew Young (XY.AI Labs, Palo Alto, CA), John Thickstun (Cornell University, Ithaca, NY), Gil Alterovitz (Brigham and Women's Hospital / Harvard Medical School, Boston, MA), Walter A. De Brouwer (Stanford University School of Medicine, Stanford, CA)
arXiv AI
1d ago

Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback

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