The paper introduces SPAR, a closed‑loop simulation platform that couples real‑time AUV control software with a higher‑level orchestration layer for fault injection, prompting, and evaluation of large language models (LLMs) in diagnosing and recovering from anomalies. SPAR enables ensemble testing of LLMs, comparing a frontier model with three locally deployable LLMs on a mass‑shift fault scenario across 480 trials, revealing that model choice significantly affects diagnostic accuracy. The study demonstrates that while the frontier model consistently ranks the correct fault mechanism among its top hypotheses, local models succeed mainly when they follow the full diagnostic procedure, and overall diagnosis and operational decisions appear decoupled in this dataset.
By Khalid Halba, Kylie Cooper, James G. Bellingham
arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.
By Jiyong Kwon, Ujin Jeon, Sooji Lee, Guang Lin
arXiv:2606. 01416v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents rely on orchestration layers that coordinate planning, retrieval, tool invocation, validation, memory, and recovery.
By Rahul Suresh Babu, Adarsh Agrawal
arXiv:2608. 14680v1 Announce Type: new Abstract: Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent messages), not of the final answer alone, yet evaluating only task outcomes reveals little about how or why a run fails.
By Chenkai Zhang, Yiran Li, Yifang Tian, Michalis Bachras, Hans-Arno Jacobsen
arXiv:2606. 31114v1 Announce Type: new Abstract: Unmanned Traffic Management (UTM) systems are cloud-based platforms designed to manage and coordinate multiple aerial vehicles remotely.
By Huaze Tang, Bill Zeng, Chao Wang, Zhenpeng Shi, Qian Zhang, Wenbo Ding
arXiv:2607. 10059v1 Announce Type: new Abstract: Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain.
By Xun Liu, Yi Evie Zhang, Vira Kasprova, Parisa Rabbani, Pardis Sadat Zahraei, Tianyu Zhang, Ali Ebrahimpour-Boroojeny, Varun Chandrasekaran
PlannerForge is a unified LLM‑agent framework that covers the entire scenario‑based testing pipeline for autonomous driving systems, from scenario generation to ADS assessment, and adds ADS enhancement and benchmarking stages. It was evaluated with ten off‑the‑shelf LLMs across all tasks and five prompt conditions, achieving best‑per‑task scores between 0.88 and 1.00 and matching commercial APIs with open‑source models such as Qwen3.6:35B. The end‑to‑end chaining retains 83% of seed queries for commercial backends and 78% for open‑source, outperforming existing tools like Scenario Factory 2.0 and BM25 in natural‑language generation, attribute realization, and physically valid edits.
whyItMatters":"PlannerForge demonstrates that a single LLM‑based system can streamline and improve the fragmented scenario‑based testing workflow for autonomous driving, achieving high performance without domain‑specific fine‑tuning."
By Yuan Gao, Sebastian M\"uller, Mattia Piccinini, Marc Kaufeld, Yuchen Zhang, Finn Rasmus Sch\"afer, Qunying Song, Johannes Betz
arXiv:2606. 14219v1 Announce Type: cross Abstract: Agentic AI can support unmanned aerial vehicle (UAV) autonomy by providing high-level recovery reasoning when local waypoint- or setpoint-based execution encounters blocked passages, repeated no-progress behavior, or mission-level ambiguity.
By Taewoo Park, Kyeonghyun Yoo, Seunghyun Yoo, Hwangnam Kim
Large language model (LLM) agents may assist flight crews with complex decisions and task execution, but existing aviation evaluations centered on static knowledge do not support systematic testing of...
arXiv:2608. 13450v1 Announce Type: cross Abstract: Autonomous vehicles depend on large safety-critical software stacks, where weaknesses reachable from adversarial inputs may affect steering, braking, or other control decisions.
By Md Wasiul Haque, Sagar Dasgupta, Mizanur Rahman, Md Rayhanur Rahman
arXiv:2608. 16349v1 Announce Type: new Abstract: Large language model (LLM) agents may assist flight crews with complex decisions and task execution, but existing aviation evaluations centered on static knowledge do not support systematic testing of procedural execution and safety compliance in interactive environments.
By Yuchen Yuan, Zhenghuang Wu, Yuangan Li, Liang Ma, Ke Li
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