The article discusses the rise of large language models (LLMs) and their growing influence on our decisions and actions. It explores the concept of agentic LLMs—LLMs that can act directly in the real world and interact with one another. The book aims to explain how these agentic LLMs function, their capabilities, and the implications for users.
arXiv:2512. 22256v2 Announce Type: replace-cross Abstract: Software issue resolution aims to address real-world issues in software repositories based on natural language descriptions provided by users, and represents a key aspect of software maintenance.
The article introduces the concept of Agentic Large Language Models, which extend traditional LLMs by enabling them to act directly in the real world and interact with one another. It outlines how these agentic LLMs function, their mechanisms for action, and the implications of their capabilities for users. The discussion highlights the growing influence of LLMs on decision-making and explores the potential shifts in how we engage with AI tools.
arXiv:2603. 02070v3 Announce Type: replace Abstract: When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elicitation process, where the human's role is to guide the AI planner according to their preferences and expertise.
arXiv:2607. 06757v1 Announce Type: new Abstract: Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making.
arXiv:2606. 30441v1 Announce Type: cross Abstract: A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS).
arXiv:2606. 19857v1 Announce Type: cross Abstract: Large language models (LLMs) are commonly prompted and interfaced with human-readable natural language, even when the intended reader is another model.
arXiv:2607. 17948v1 Announce Type: new Abstract: Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents.
arXiv:2607. 18806v1 Announce Type: new Abstract: This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents.
arXiv:2506. 12339v2 Announce Type: replace-cross Abstract: We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions.
arXiv:2606. 29014v1 Announce Type: new Abstract: Recent advancements in generative artificial intelligence (AI) and large language models (LLMs) have shown significant promise in automating complex reasoning, summarization, and question-answering tasks.
The paper introduces Markovian generation chains, an iterative inference process in large language models where each step uses a prompt template and the previous output as input, excluding prior data. Experiments in iterative rephrasing and round‑trip translation show that outputs either converge to a small recurrent set or continue generating novel sentences within a finite horizon. Sentence‑level Markov chain modeling reveals that the process can either increase or reduce diversity, depending on factors such as temperature and the initial input sentence.