arXiv AI
Aug 11
Lingjing: A Simulation Testbed for Multi-Agent Embodied Tasks in Open-Ended Cities
arXiv:2608. 08045v1 Announce Type: new Abstract: Urban embodied intelligence requires coordination among heterogeneous agents (e.
AURORA: A Natural Language-Driven Agentic Framework for Understanding, Reasoning, and Orchestrating Reliable Air-Ground Co-Simulation
AURORA is a natural‑language‑driven framework that treats air‑ground scenario generation as a compilation process with verification. It introduces the Air‑Ground Scenario Graph (AGSG), a typed intermediate representation linking agents, missions, events, communication, and success conditions, enabling joint grounding, temporal planning, pre‑execution checks, runtime verification, failure localization, and bounded repair. The authors also present AURORA‑Bench to evaluate not only execution but faithful realization of requested interactions, showing that structured execution and runtime verification improve reliability and that explicit intermediate representations facilitate verifiable and repairable co‑simulation.
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arXiv AI
Sep 25
MOOSEnger: A Simulation-Aware AI Agent Framework for the MOOSE Ecosystem
MOOSEnger is a simulation‑aware AI agent framework designed for the MOOSE ecosystem, integrating an interchangeable reasoning model with domain knowledge, revised simulation artifacts, MOOSE‑specific validation, and executable solver feedback. Its generate‑check‑repair‑run workflow uses MOOSE knowledge retrieval, HIT‑aware parsing, syntax metadata, diagnostics, and revision‑controlled authoring to bind evidence to each input revision and guide bounded repair before acceptance. Across 200 prompts, MOOSEnger raises executable success from 5% to 89.5% with GPT‑5.2 and from 0% to 76.5% with Gemma 4 31B, and a ten‑case benchmark shows all generated inputs meet semantic alignment, with eight also meeting numerical‑accuracy criteria.
arXiv AI
Aug 18
AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment
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.
Hugging Face Trending Papers
Aug 17
AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment
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 AI
2d ago
OrbitTAMP: Grounding Language Models for Task and Motion Planning in Spacecraft Rendezvous
arXiv:2610.01093v1 Announce Type: cross Abstract: Spacecraft rendezvous and proximity operations (RPO) are currently planned through an expertise-intensive process in which engineers translate high-l...
arXiv AI
Aug 11
AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems
arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.
arXiv AI
Aug 5
UrbanAgent: A Tool-Augmented Agent for Cross-System Urban Tasks
arXiv:2608. 03018v1 Announce Type: new Abstract: Modern cities rely on an increasing number of digital services to operate, but residents' daily needs are still difficult to meet.
arXiv AI
Jul 22
SENTINEL: A Multi-Level Formal Framework for Safety Evaluation of Foundation Model-based Embodied Agents
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.
arXiv AI
Jun 30
AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control
arXiv:2603. 26005v2 Announce Type: replace Abstract: Grid-interactive building control has emerged as a promising approach for improving demand-side flexibility in modern power systems.
arXiv AI
Jul 28
CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning
arXiv:2505. 18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other.
arXiv AI
Jun 17
Blueprint First, Model Second: A Framework for Deterministic LLM Workflow
arXiv:2508. 02721v2 Announce Type: replace-cross Abstract: While powerful, the inherent non-determinism of large language model (LLM) agents limits their application in structured operational environments where procedural fidelity and predictable execution are strict requirements.
arXiv AI
Jul 21
Composable Verification Pipelines for Multi-Agent Systems
arXiv:2607. 16266v1 Announce Type: cross Abstract: Existing approaches for reasoning about action and change provide expressive semantics for modeling dynamic systems, in most cases built on top of logic programming systems.