Agentic LLM Framework for Adaptive Decision Discourse

• URL: http://arxiv.org/abs/2502.10978v1
• Date: Sun, 16 Feb 2025 03:46:37 GMT
• Title: Agentic LLM Framework for Adaptive Decision Discourse
• Authors: Antoine Dolant, Praveen Kumar,
• Abstract summary: This study introduces a real-world inspired agentic Large Language Models (LLMs) framework. Unlike traditional decision-support tools, the framework emphasizes dialogue, trade-off exploration, and the emergent synergies generated by interactions among agents. Results reveal how the breadth-first exploration of alternatives fosters robust and equitable recommendation pathways.
• Score: 2.4919169815423743
• License:
• Abstract: Effective decision-making in complex systems requires synthesizing diverse perspectives to address multifaceted challenges under uncertainty. This study introduces a real-world inspired agentic Large Language Models (LLMs) framework, to simulate and enhance decision discourse-the deliberative process through which actionable strategies are collaboratively developed. Unlike traditional decision-support tools, the framework emphasizes dialogue, trade-off exploration, and the emergent synergies generated by interactions among agents embodying distinct personas. These personas simulate diverse stakeholder roles, each bringing unique priorities, expertise, and value-driven reasoning to the table. The framework incorporates adaptive and self-governing mechanisms, enabling agents to dynamically summon additional expertise and refine their assembly to address evolving challenges. An illustrative hypothetical example focused on extreme flooding in a Midwestern township demonstrates the framework's ability to navigate uncertainty, balance competing priorities, and propose mitigation and adaptation strategies by considering social, economic, and environmental dimensions. Results reveal how the breadth-first exploration of alternatives fosters robust and equitable recommendation pathways. This framework transforms how decisions are approached in high-stakes scenarios and can be incorporated in digital environments. It not only augments decision-makers' capacity to tackle complexity but also sets a foundation for scalable and context-aware AI-driven recommendations. This research explores novel and alternate routes leveraging agentic LLMs for adaptive, collaborative, and equitable recommendation processes, with implications across domains where uncertainty and complexity converge.

Related papers

• EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning [69.55982246413046]
  We propose explicit policy optimization (EPO) for strategic reasoning. EPO provides strategies in open-ended action space and can be plugged into arbitrary LLM agents to motivate goal-directed behavior. Experiments across social and physical domains demonstrate EPO's ability of long-term goal alignment.
  arXiv  Detail & Related papers  (2025-02-18T03:15:55Z)
• Do as We Do, Not as You Think: the Conformity of Large Language Models [46.23852835759767]
  This paper presents a study on conformity in large language models (LLMs) driven collaborative AI systems. We focus on three aspects: the existence of conformity, the factors influencing conformity, and potential mitigation strategies. Our analysis delves into factors influencing conformity, including interaction time and majority size, and examines how the subject agent rationalizes its conforming behavior.
  arXiv  Detail & Related papers  (2025-01-23T04:50:03Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Position: Foundation Agents as the Paradigm Shift for Decision Making [24.555816843983003]
  We advocate for the construction of foundation agents as a transformative shift in the learning paradigm of agents. We specify the roadmap of foundation agents from large interactive data collection or generation to self-supervised pretraining and adaptation.
  arXiv  Detail & Related papers  (2024-05-27T09:54:50Z)
• Enhancing Decision-Making in Optimization through LLM-Assisted Inference: A Neural Networks Perspective [1.0420394952839245]
  This paper explores the seamless integration of Generative AI (GenAI) and Evolutionary Algorithms (EAs) Focusing on the transformative role of Large Language Models (LLMs), our study investigates the potential of LLM-Assisted Inference to automate and enhance decision-making processes.
  arXiv  Detail & Related papers  (2024-05-12T08:22:53Z)
• Organizing a Society of Language Models: Structures and Mechanisms for Enhanced Collective Intelligence [0.0]
  This paper introduces a transformative approach by organizing Large Language Models into community-based structures. We investigate different organizational models-hierarchical, flat, dynamic, and federated-each presenting unique benefits and challenges for collaborative AI systems. The implementation of such communities holds substantial promise for improve problem-solving capabilities in AI.
  arXiv  Detail & Related papers  (2024-05-06T20:15:45Z)
• LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
  Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly. We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models. It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations [0.0]
  This article explores the influence of computational entities based on multi-agent systems theory (SMA) and large language models (LLM) on human user interaction. In our approach we employ agents developed from large language models (LLM), each with distinct prototyping that considers behavioral elements. We demonstrate the potential of developing agents useful for organizational strategies, based on multi-agent system theories (SMA) and innovative uses based on large language models (LLM based)
  arXiv  Detail & Related papers  (2024-03-12T15:56:10Z)
• Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning [10.67134969207797]
  Agent-based models (ABMs) stand as an essential paradigm for proposing and validating hypothetical solutions or policies. Large language models (LLMs) encapsulating cross-domain knowledge and programming proficiency could potentially alleviate the difficulty of this process. We present SAGE, a general solution-oriented ABM generation framework designed for automatic modeling and generating solutions for targeted problems.
  arXiv  Detail & Related papers  (2024-02-04T07:59:06Z)
• HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
  HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
  arXiv  Detail & Related papers  (2024-01-23T18:59:43Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.