Technological Advancements in Digital Collaboration with Generative AI and Large Language Models

Permanent URI for this collectionhttps://hdl.handle.net/10125/112412

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    Agent Reasoning Tools (ARTs): A Tool Definition Approach for Empower LLM-based Agent Systems
    (2026-01-06) Tao, Jie; Zhou, Lina
    The emergence of LLM-based agentic systems is transforming human-technology interaction by enabling proactive collaboration. However, LLMs often rely on external tools due to their lack direct interaction with environments or access to up-to-date information. This makes effective tool definition and management essential, yet such efforts are challenged by rigidity, overhead, and complexity of logic specification. Current methods often focus on external capabilities, overlooking the enhancement of an agent's internal reasoning. This research introduces Agent Reasoning Tools (ARTs) to address these challenges. ARTs are designed to reduce rigidity and overhead while enabling flexible, human-understandable logic definitions that enhance human-AI collaboration. Evaluating ARTs on aspect term extraction shows highly competitive performance. They represent a significant step toward more flexible, transparent, and user-friendly agentic systems.
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    LLM-Based Policy Generation for Distributed Adaptive Systems
    (2026-01-06) Carvalho, Marco; Nembhard, Fitzroy
    In a multi-agent system, the actions of one or more agents may lead to undesirable results that may affect the entire system. As a result, it is important to have controls in place to govern the actions of these agents. Polices have commonly been used to establish constraints to regulate the actions that are permitted or prohibited within a system. However, it is challenging for policy authors to manually formulate and verify policies in complex multi-agent systems. In this work, we propose a framework that applies large language models (LLMs) to policy generation and management to simplify the work of humans in the loop. LLMs have permeated many aspects of human life in a short space of time. They provide powerful capabilities in natural language processing such as understanding input, generating output, and classifying data. Since the Ontology Web Language (OWL), which is the cornerstone of the semantic web, is widely used to describe declarative policies, we use a set of incremental experiments to demonstrate that pre-trained transformers can be used to generate ontology-driven polices, thus enabling practitioners to better manage adaptive systems.