arXiv AI

FacProcessTwin: An LLM-Based System for Process Twin Development

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

arXiv AI

Jul 31

On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discovery

arXiv:2604. 22455v2 Announce Type: replace Abstract: A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defines its maximal behavioral boundaries.

By Anti Alman, Izack Cohen, Avigdor Gal, Fabrizio Maria Maggi, Marco Montali

agents

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)

llms safety

arXiv AI

Aug 5

Evaluating LLM Trade-offs for Enterprise Automation: Lessons from Workflow Generation in a Production Enterprise Platform

arXiv:2608. 03311v1 Announce Type: cross Abstract: Enterprise compliance management requires rapid adaptation to evolving regulatory frameworks (e.

By Xavier Wrenn, Radoslav Raykov, Aleksandar Angelov, Hirokuni Kitahara, Yuji Watanabe, Anca Sailer

llms

arXiv AI

Aug 12

A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents

arXiv:2605. 20173v2 Announce Type: replace Abstract: Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a first-class architectural object.

By Vasundra Srinivasan

llms agents

arXiv AI

Jul 17

Beyond Generalist LLMs: Specialist Agentic Systems for Structured Code Workflow Execution

arXiv:2607. 14456v1 Announce Type: cross Abstract: Large Language Models (LLMs) have accelerated the adoption of software development agents, now widely available as Integrated Development Environment (IDE) extensions and standalone applications.

By Harris Borman, Herman Wandabwa, Fusun Yu, Sandeepa Kannangara, Justin Liu, Anna Leontjeva, Ritchie Ng

llms agents

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

llms agents safety

arXiv AI

Jul 21

DataFlow-Harness: A Grounded Code-Agent Platform for Constructing Editable LLM Data Pipelines

arXiv:2607. 16617v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to automate data-processing workflows, yet coding agents typically produce scripts that are not automatically materialized as persistent, editable platform artifacts.

By Runming He, Zhen Hao Wong, Hao Liang, Zimo Meng, Chengyu Shen, Xiaochen Ma, Wentao Zhang

llms agents benchmarks

arXiv Machine Learning

Jul 10

Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems

arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.

By Kalle Kujanp\"a\"a, Ning Liu, Shahnawaz Alam, Yeshwanth Reddy Sura, Tianyu Yang, Kristina Klinkner, Shervin Malmasi

llms agents benchmarks

arXiv AI

Jul 1

Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models

arXiv:2606. 31614v1 Announce Type: cross Abstract: Engineering specifications such as interlocks, alarm rationalization tables, and cause-and-effect (C&E) matrices remain central to process control and safety, yet their creation is still predominantly manual, document-driven, and prone to inconsistency.

By Javal Vyas, Milapji Singh Gill, Mehmet Mercang\"oz

llms safety

arXiv Machine Learning

Jun 29

From Detection to Action: Using LLM Agents for Fault-Tolerant Control

arXiv:2606. 28011v1 Announce Type: cross Abstract: We propose an agentic Large Language Model (LLM) framework for active Fault-Tolerant Control (FTC) that transforms fault detection outputs into constraint-aware recovery actions grounded in plant-specific knowledge.

By Javal Vyas, Milapji Singh Gill, Artan Markaj, Felix Gehlhoff, Mehmet Mercang\"oz

llms rag agents benchmarks safety

arXiv AI

Jun 15

SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model

arXiv:2606. 14574v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments.

By Xiaoxin Lu, Ranran Haoran Zhang, Rui Zhang

llms agents benchmarks

arXiv AI

Jun 9

Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models

arXiv:2606. 07808v1 Announce Type: new Abstract: Reasoning language models deployed in agentic workflows must follow an instruction hierarchy: when instructions from different sources conflict, the model should obey the highest-privilege applicable instruction.

By Sanjay Kariyappa, G. Edward Suh

llms agents benchmarks

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