Papers by Junlong Ren

3 papers
VistaWise: Building Cost-Effective Agent with Cross-Modal Knowledge Graph for Minecraft (2025.emnlp-main)

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Challenge: Large language models (LLMs) have shown significant promise in embodied decision-making tasks within virtual open-world environments, but lack domain-specific knowledge.
Approach: They propose a cost-effective agent framework that integrates cross-modal domain knowledge and finetunes a dedicated object detection model for visual analysis.
Outcome: The proposed framework reduces the requirement for domain-specific training data from millions of samples to a few hundred.
CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks (2025.findings-emnlp)

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Challenge: Existing work on multi-agent collaborative tasks in Minecraft is limited due to inefficiency and limited fault tolerance.
Approach: They propose a framework that incorporates causality to manage dependencies among subtasks.
Outcome: The proposed framework achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft.
From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models (2026.findings-acl)

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Challenge: Existing definitions of streaming LLMs are fragmented and lack a systematic taxonomy . large language models are pre-trained on static and full-context corpora .
Approach: They propose a systematic taxonomy of current streaming Large Language Models and propose underlying methodologies for streaming LLMs.
Outcome: The proposed model is based on data flow and dynamic interaction to clarify existing ambiguities.

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