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.
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.
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.
arXiv:2606. 16010v1 Announce Type: cross Abstract: Large language models have achieved impressive performance on reasoning tasks spanning mathematics, science, programming, and commonsense inference.
arXiv:2605. 09045v2 Announce Type: replace Abstract: Agentic frameworks are the software layer through which AI agents act in the world.
arXiv:2606. 00220v1 Announce Type: cross Abstract: Formal methods provide rigorous accounts of program behavior, but practical software engineering often works through executable libraries, tests, and incremental design.
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:2603. 24747v2 Announce Type: replace Abstract: The emergence of large language model agents capable of invoking external tools has created urgent need for formal verification of agent protocols.
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
arXiv:2606. 01008v1 Announce Type: cross Abstract: We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks.