Optimizing Collaboration of LLM based Agents for Finite Element Analysis

• URL: http://arxiv.org/abs/2408.13406v1
• Date: Fri, 23 Aug 2024 23:11:08 GMT
• Title: Optimizing Collaboration of LLM based Agents for Finite Element Analysis
• Authors: Chuan Tian, Yilei Zhang,
• Abstract summary: This paper investigates the interactions between multiple agents within Large Language Models (LLMs) in the context of programming and coding tasks. We utilize the AutoGen framework to facilitate communication among agents, evaluating different configurations based on the success rates from 40 random runs for each setup.
• Score: 1.5039745292757671
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper investigates the interactions between multiple agents within Large Language Models (LLMs) in the context of programming and coding tasks. We utilize the AutoGen framework to facilitate communication among agents, evaluating different configurations based on the success rates from 40 random runs for each setup. The study focuses on developing a flexible automation framework for applying the Finite Element Method (FEM) to solve linear elastic problems. Our findings emphasize the importance of optimizing agent roles and clearly defining their responsibilities, rather than merely increasing the number of agents. Effective collaboration among agents is shown to be crucial for addressing general FEM challenges. This research demonstrates the potential of LLM multi-agent systems to enhance computational automation in simulation methodologies, paving the way for future advancements in engineering and artificial intelligence.

Related papers

• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process. We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation [49.27250832754313]
  We present AgentCOT, a llm-based autonomous agent framework. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. We introduce two new strategies to enhance the performance of AgentCOT.
  arXiv  Detail & Related papers  (2024-09-19T02:20:06Z)
• LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Integration of Multi Active/Passive Core-Agents [0.0]
  We propose a novel LLM-based Agent Unified Modeling Framework (LLM-Agent-UMF) Our framework distinguishes between the different components of an LLM-based agent, setting LLMs and tools apart from a new element, the core-agent. We evaluate our framework by applying it to thirteen state-of-the-art agents, thereby demonstrating its alignment with their functionalities.
  arXiv  Detail & Related papers  (2024-09-17T17:54:17Z)
• GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI [64.57616646552869]
  This paper explores collaborative AI systems that use to enhance performance to integrate models, data sources, and pipelines to solve complex and diverse tasks. We introduce GenAgent, an LLM-based framework that automatically generates complex, offering greater flexibility and scalability compared to monolithic models. The results demonstrate that GenAgent outperforms baseline approaches in both run-level and task-level evaluations.
  arXiv  Detail & Related papers  (2024-09-02T17:44:10Z)
• FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models [50.331708897857574]
  We introduce FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications. FactorLLM achieves comparable performance to the source model securing up to 85% model performance while obtaining over a 30% increase in inference speed.
  arXiv  Detail & Related papers  (2024-08-15T16:45:16Z)
• CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models [8.123272461141815]
  We introduce the TinyAgent model, trained on a meticulously curated high-quality dataset. We also present the Collaborative Multi-Agent Tuning (CMAT) framework, an innovative system designed to augment language agent capabilities. In this research, we propose a new communication agent framework that integrates multi-agent systems with environmental feedback mechanisms.
  arXiv  Detail & Related papers  (2024-04-02T06:07:35Z)
• Large Language Model-based Human-Agent Collaboration for Complex Task Solving [94.3914058341565]
  We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving. We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
  arXiv  Detail & Related papers  (2024-02-20T11:03:36Z)
• 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)
• MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge [0.6708125191843434]
  A set of AI agents can solve mechanics tasks, here demonstrated for elasticity problems, via autonomous collaborations. A two-agent team can effectively write, execute and self-correct code, in order to apply finite element methods to solve classical elasticity problems. For more complex tasks, we construct a larger group of agents with enhanced division of labor among planning, formulating, coding, executing and criticizing the process and results.
  arXiv  Detail & Related papers  (2023-11-14T13:49:03Z)
• Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization [59.39113350538332]
  Large language model (LLM) agents have been shown effective on a wide range of tasks, and by ensembling multiple LLM agents, their performances could be further improved. Existing approaches employ a fixed set of agents to interact with each other in a static architecture. We build a framework named Dynamic LLM-Agent Network ($textbfDyLAN$) for LLM-agent collaboration on complicated tasks like reasoning and code generation.
  arXiv  Detail & Related papers  (2023-10-03T16:05:48Z)
• TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage [28.554981886052953]
  Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks. This paper proposes a structured framework tailored for LLM-based AI Agents.
  arXiv  Detail & Related papers  (2023-08-07T09:22:03Z)

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.