arXiv:2603. 29152v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) offer a vast design space, and as such, computational simulations play a critical role in predicting their structural and physicochemical properties.
By Jaewoong Lee, Taeun Bae, Jihan Kim
The paper introduces complete cyclic subtask graphs for large language model agents, enabling a workflow controller where all subtasks are fully connected and a unified agent selects transitions based on natural‑language criteria. It evaluates task‑specific and benchmark‑generic cyclic graphs on TextCraft, ALFWorld, and Finance‑Agent, comparing them to ReAct and dependency‑directed workflows, and identifies three distinct workflow signatures that influence the effectiveness of cyclic routing. The study also provides a workflow‑signature matrix, robustness analysis, token‑cost accounting, and failure‑mode structure, concluding that cyclic subtask graphs serve as a diagnostic tool to determine when flexible backtracking is worthwhile versus when simpler controllers suffice.
By Luay Gharzeddine, Samer Saab Jr
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
The paper introduces AutoMat, a benchmark designed to test large language model (LLM) coding agents on their ability to reproduce claims from computational materials science. AutoMat presents three challenges: reconstructing underspecified procedures, navigating specialized toolchains, and assessing whether the evidence supports a claim. Experiments show that current LLM agents achieve low success rates, with the best setting reaching only 53%, and failures stem mainly from incomplete procedures, methodological deviations, and execution fragility.
By Ziyang Huang, Yi Cao, Ali K. Shargh, Jing Luo, Ruidong Mei, Mohd Zaki, Zhan Liu, Wyatt Bunstine, William Jurayj, Somdatta Goswami, Tyrel McQueen, Michael Shields, Jaafar El-Awady, Paulette Clancy, Benjamin Van Durme, Nicholas Andrews, William Walden, Daniel Khashabi
arXiv:2606. 21228v2 Announce Type: replace Abstract: The capabilities of frontier Large Language Models (LLMs) continue to advance, with different providers increasingly specializing in distinct domains.
By Yujin Tang, Edoardo Cetin, Jinglue Xu, Qi Sun, Stefan Nielsen, Vincent Richard, Haruto Goda, Iaroslav Tymchenko, Nhan Nguyen, Hyunin Lee, Mari Ashiga, Shashank Kotyan, So Kuroki, Tarin Clanuwat
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
By Shuo Hao, You Lu, Bihuan Chen, Xin Peng