LLM Bazaar: A Service Design for Supporting Collaborative Learning with an LLM-Powered Multi-Party Collaboration Infrastructure

• URL: http://arxiv.org/abs/2510.18877v1
• Date: Fri, 12 Sep 2025 01:25:49 GMT
• Title: LLM Bazaar: A Service Design for Supporting Collaborative Learning with an LLM-Powered Multi-Party Collaboration Infrastructure
• Authors: Zhen Wu, Jiaxin Shi, R. Charles Murray, Carolyn Rosé, Micah San Andres,
• Abstract summary: Large language models (LLMs) offer new possibilities for fostering critical thinking and collaborative problem solving.<n>In this work, we begin with an open source collaboration support architecture called Bazaar and integrate an LLM-agent shell.<n>This design and infrastructure paves the way for exploring how tailored LLM-empowered environments can reshape collaborative learning outcomes and interaction patterns.
• Score: 15.92352456356919
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For nearly two decades, conversational agents have played a critical role in structuring interactions in collaborative learning, shaping group dynamics, and supporting student engagement. The recent integration of large language models (LLMs) into these agents offers new possibilities for fostering critical thinking and collaborative problem solving. In this work, we begin with an open source collaboration support architecture called Bazaar and integrate an LLM-agent shell that enables introduction of LLM-empowered, real time, context sensitive collaborative support for group learning. This design and infrastructure paves the way for exploring how tailored LLM-empowered environments can reshape collaborative learning outcomes and interaction patterns.

Related papers

• Impact of LLMs on Team Collaboration in Software Development [0.0]
  Large Language Models (LLMs) are increasingly being integrated into software development processes.<n>This paper investigates how LLMs affect team collaboration throughout the Software Development Life Cycle.
  arXiv  Detail & Related papers  (2025-10-07T18:16:17Z)
• OPEN-THEATRE: An Open-Source Toolkit for LLM-based Interactive Drama [62.00761178362677]
  Open-Theatre is the first open-source toolkit for experiencing and customizing LLM-based interactive drama.<n>It refines prior work with an efficient multi-agent architecture and a hierarchical retrieval-based memory system.
  arXiv  Detail & Related papers  (2025-09-20T14:53:14Z)
• Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments [9.128357856312372]
  Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities.<n>Existing LLM-based planning algorithms are limited by weak adaptation capabilities to multi-agent embodied scenarios.<n>We introduce a framework that enables LLM agents to learn and evolve both before and during test time.
  arXiv  Detail & Related papers  (2025-06-08T17:32:03Z)
• Collaborating Action by Action: A Multi-agent LLM Framework for Embodied Reasoning [12.923902619187274]
  This work studies how LLMs can adaptively collaborate to perform complex embodied reasoning tasks.<n> MINDcraft is a platform built to enable LLM agents to control characters in the open-world game of Minecraft.<n>An experimental study finds that the primary bottleneck in collaborating effectively for current state-of-the-art agents is efficient natural language communication.
  arXiv  Detail & Related papers  (2025-04-24T21:28:16Z)
• When One LLM Drools, Multi-LLM Collaboration Rules [98.71562711695991]
  We argue for multi-LLM collaboration to better represent the extensive diversity of data, skills, and people.<n>We organize existing multi-LLM collaboration methods into a hierarchy, based on the level of access and information exchange.<n>We envision multi-LLM collaboration as an essential path toward compositional intelligence and collaborative AI development.
  arXiv  Detail & Related papers  (2025-02-06T21:13:44Z)
• Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models [32.336273322481276]
  Despite their diverse capabilities, Large Language Models (LLMs) exhibit varying strengths and weaknesses. To address these challenges, recent studies have explored collaborative strategies for LLMs. This paper provides a comprehensive overview of this emerging research area, highlighting the motivation behind such collaborations.
  arXiv  Detail & Related papers  (2024-07-08T16:29:08Z)
• Simulating Classroom Education with LLM-Empowered Agents [48.26286735827104]
  Large language models (LLMs) have been applied across various intelligent educational tasks to assist teaching.<n>We propose SimClass, a multi-agent classroom simulation teaching framework.<n>We recognize representative class roles and introduce a novel class control mechanism for automatic classroom teaching.
  arXiv  Detail & Related papers  (2024-06-27T14:51:07Z)
• Embodied LLM Agents Learn to Cooperate in Organized Teams [46.331162216503344]
  Large Language Models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making. This paper introduces a framework that imposes prompt-based organization structures on LLM agents to mitigate these problems.
  arXiv  Detail & Related papers  (2024-03-19T06:39:47Z)
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
  Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs. We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
  arXiv  Detail & Related papers  (2024-01-14T16:17:07Z)
• Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
  Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes. We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-30T07:11:39Z)
• Building Cooperative Embodied Agents Modularly with Large Language Models [104.57849816689559]
  We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments. We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework. Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
  arXiv  Detail & Related papers  (2023-07-05T17:59:27Z)

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.