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LARP: Language-Agent Role Play for Open-World Games

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arxiv 2312.17653 v1 pith:MRYMWI4S submitted 2023-12-24 cs.AI

classification cs.AI
keywords agentslanguagelarpopen-worldgamesmemorypersonalitiesvarious
verification ladder T0 review T1 audit T2 compute T3 formal
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Language agents have shown impressive problem-solving skills within defined settings and brief timelines. Yet, with the ever-evolving complexities of open-world simulations, there's a pressing need for agents that can flexibly adapt to complex environments and consistently maintain a long-term memory to ensure coherent actions. To bridge the gap between language agents and open-world games, we introduce Language Agent for Role-Playing (LARP), which includes a cognitive architecture that encompasses memory processing and a decision-making assistant, an environment interaction module with a feedback-driven learnable action space, and a postprocessing method that promotes the alignment of various personalities. The LARP framework refines interactions between users and agents, predefined with unique backgrounds and personalities, ultimately enhancing the gaming experience in open-world contexts. Furthermore, it highlights the diverse uses of language models in a range of areas such as entertainment, education, and various simulation scenarios. The project page is released at https://miao-ai-lab.github.io/LARP/.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mastering PokeGym: Graph-Guided Multimodal Evolution at Test Time

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    The manuscript body introduces PokeGym, a vision-only automated 3D-game benchmark, while the abstract claims a G-EvoMAC method and 60.18% success rate absent from the body.

  2. STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning

    cs.AI 2025-02 conditional novelty 5.0 of 10

    A spatio-temporal memory agent combining a textual history summarizer, a spatial knowledge graph, and a planner-critic loop outperforms ReAct, Reflexion, and AdaPlanner on TextWorld cooking tasks.

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