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.
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arXiv:2608. 03609v1 Announce Type: new Abstract: Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world workflows where they act on persistent operational data.
arXiv:2606. 06523v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence.
arXiv:2606. 14790v1 Announce Type: cross Abstract: LLM-based multi-agent systems increasingly coordinate planning, reasoning, tool use, and human interaction, yet their reliability remains limited.
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.
arXiv:2607. 17780v1 Announce Type: cross Abstract: ETAS is a programming language for agent systems that treats model-backed agents, tool calls, prompts, typed memory, human approvals, policies, and execution traces as semantic program elements rather than library conventions.
arXiv:2604. 11556v2 Announce Type: replace-cross Abstract: LLM-assisted software development has become increasingly prevalent, and can generate large-scale systems, such as compilers.