Challenge: Existing interfaces lack intuitive, contextaware capabilities needed for easily navigating complex architectural components in 3D spaces.
Approach: They propose a virtual assistant that allows users to interact with the BIM file in a VR environment.
Outcome: The proposed assistant could allow users to interact with the BIM file in a virtual reality environment and perform complex actions.

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CARD: Cross-modal Agent Framework for Generative and Editable Residential Design (2025.emnlp-main)

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Challenge: Architectural design automation has made significant progress, but the complexity of open-world environments makes residential design a challenging task.
Approach: They propose a framework that leverages a system of specialized cross-modal agents to adapt to open-world residential design.
Outcome: The proposed framework enables users to generate and edit residential design without requiring specialized expertise.
SIMMC-VR: A Task-oriented Multimodal Dialog Dataset with Situated and Immersive VR Streams (2023.acl-long)

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Challenge: Existing models lack a large-scale benchmark to capture user–assistant interactions . et al., 2022: 145-160.
Approach: They propose a video-grounded task-oriented dialog dataset that captures real-world AI-assisted user scenarios in VR.
Outcome: The proposed dataset captures real-world AI-assisted user scenarios in VR.
INDOORWORLD : Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment (2025.findings-emnlp)

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Challenge: Existing virtual environments for LLM agent research focus on task solving or social simulation . existing environments for virtual environments lack physical grounding of social behaviors .
Approach: They propose a virtual environment that tightly integrates physical and social dynamics . IndoorWorld is a heterogeneous multi-agent environment that integrates social and physical dynamics based on a simulation of physical environments .
Outcome: The proposed environment integrates physical and social dynamics into a heterogeneous multi-agent environment.
Open-Theatre: An Open-Source Toolkit for LLM-based Interactive Drama (2025.emnlp-demos)

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Challenge: Existing tools for creating, modifying, and experimenting with interactive dramas are limited.
Approach: They propose an open-source toolkit for creating configurable LLM-based interactive drama.
Outcome: The proposed toolkit enhances narrative coherence and realistic behavior in interactions with agents.
AdaMARP: An Adaptive Multi-Agent Interaction Framework for General Immersive Role-Playing (2026.findings-acl)

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Challenge: Existing LLMs lack immersion and adaptability, resulting in limited character orchestration and on-the-fly character introduction.
Approach: They propose an LLM-based framework that allows actors to interact with users in an ongoing narrative.
Outcome: The proposed framework outperforms commercial LLMs in character consistency, environment grounding, and narrative coherence.
Connecting Language and Vision to Actions (P18-5)

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Challenge: Recent advances in language and vision have made incredible progress in describing images and interacting with visual content in a physical or embodied environment.
Approach: This tutorial will provide an overview of the growing number of multimodal tasks and datasets that combine textual and visual understanding.
Outcome: This tutorial will review the state-of-the-art approaches to selected tasks such as image captioning, visual question answering and visual dialog.
LEGENT: Open Platform for Embodied Agents (2024.acl-demos)

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Challenge: Existing integrations of large language models and large multimodal models are limited . Existing platforms for developing embodied agents are limited and limited based on open-source software.
Approach: They propose an open platform for developing embodied agents using LLMs and LMMs.
Outcome: The proposed platform surpasses GPT-4V in embodied tasks with its model training on LEGENT data.
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)

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Challenge: Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks.
Approach: They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment.
Outcome: The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment.
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

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Challenge: Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach: They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome: The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI (2022.emnlp-main)

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Challenge: Current task-oriented dialogue systems focus on multi-turn text/speech interaction, then call back-end APIs to perform task.
Approach: They propose a GUI-based task-oriented dialogue system that can perform GUI operations on real APPs without invoking TOD-specific backend APIs.
Outcome: The proposed GUI-based task-oriented dialogue system can perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs.

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