arXiv:2606. 27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliability.
By Jiajing Zhang, Jiamei Jiang, Chenyang Zhang, Feifei Mo, Linjing Li, Daniel Zeng
PLCWorld is a closed‑loop execution environment and benchmark that evaluates large language model (LLM)–generated programmable logic controller (PLC) programs by coupling Structured Text (ST) execution with simulated plant responses and sensor feedback. It includes 100 synthetic tasks and 473 task‑condition pairs across Motion Control and Material Handling, with difficulty levels based on control‑dependency scope. The benchmark reports Task Success and Safety Violation separately and validates results through practitioner review, reference programs, counterexamples, and comparisons with independent ST runtimes, revealing performance differences among six LLMs and four generation‑and‑verification workflows.
By Yunji Kim, Yunseok Lee, Hyunwoo Seo, Jaerim Choi, Woojin Lee
arXiv:2510. 12985v3 Announce Type: replace Abstract: We present SENTINEL, a framework for formally evaluating the physical safety of foundation model (FM)-based embodied agents.
By Simon Sinong Zhan, Philip Wang, Yao Liu, Yiyan Peng, Zinan Wang, Qineng Wang, Zhian Ruan, Xiangyu Shi, Xinyu Cao, Frank Yang, Zhenyang Ni, Kangrui Wang, Ruohan Zhang, Huajie Shao, Manling Li, Qi Zhu
The paper introduces LLM-Falsifier, a large language model–based method for falsifying cyber‑physical system specifications written in Signal Temporal Logic (STL). By exposing the LLM to semantic cues such as natural‑language names, output trajectories, and critical‑time witnesses, the approach performs smarter, sample‑efficient robustness searches. On ARCH‑COMP benchmarks, LLM‑Falsifier outperforms existing tools across 14 of 21 specifications, requiring fewer simulations to find counterexamples.
By Ali ArjomandBigdeli, Jiawei Zhou, Stanley Bak
arXiv:2502. 19135v2 Announce Type: replace Abstract: We present PLANTOR, a framework for generating and executing multi-robot task plans from natural-language task descriptions through LLM-assisted knowledge-base construction.
By Enrico Saccon, Matteo Saveriano, Edoardo Lamon, Luigi Palopoli, Marco Roveri
The paper introduces control‑data flow separation to improve prompt optimization in multi‑agent large language model systems. By representing execution protocols as typed, validated program objects and keeping task‑relevant content as unstructured language, the method prevents prompt edits from corrupting critical routing, formatting, or termination signals. Experiments on synthetic reasoning, collaborative review generation, and insurance rating workflows show that this approach maintains 100% protocol validity while consistently enhancing task performance.
By Wentao Zhang, Syed Shariyar Murtaza, Junaid Ahmad Bhatti, Utkarsh Soni, Yifan Nie, Eugene Wen, Yuntian Deng