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From the 2 of 9 linked papers with an AI index.

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20242026
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cs.CL2026

Rushes: A Human Preference Dataset for Pluralistic Alignment

Michael Xu, Jorge Leandro, Sudha Rao +5

We introduce Rushes, a dataset and benchmark for studying revealed human engagement preferences in interactive narrative environments. Rushes is collected through a game interface…

cs.CL2026

GFlowRL: Scaling Distribution-Matching RL to Large Language Models

Xiaodong Liu, Michael Xu, Jack W. Stokes +3

The paper introduces GFlowRL, a simplified GFlowNet‑style reinforcement learning method that removes the learned partition function and uses an in‑batch Monte Carlo estimate, enabl…

cs.CL2024

Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft

Sudha Rao, Weijia Xu, Michael Xu +5

The use of generative AI in video game development is on the rise, and as the conversational and other capabilities of large language models continue to improve, we expect LLM-driv…

cs.CL2024

GENEVA: GENErating and Visualizing branching narratives using LLMs

Jorge Leandro, Sudha Rao, Michael Xu +4

Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The narratives of these may take years to write and typically involve a large creative team. In this work, w…

cs.CL2024

Player-Driven Emergence in LLM-Driven Game Narrative

Xiangyu Peng, Jessica Quaye, Sudha Rao +10

We explore how interaction with large language models (LLMs) can give rise to emergent behaviors, empowering players to participate in the evolution of game narratives. Our testbed…