Dream-Cubed releases a billion-scale voxel dataset and 3D diffusion models that generate controllable Minecraft worlds by operating directly on blocks.
Soemers, and Cameron Browne
7 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
Players exhibit consistent flexibility or specialization behavior across two games with conflicting performance incentives, indicating individual agency dominates structural differences.
Prevent-Jack fuses six local behaviors into a context steering framework for swarms of heavy articulated vehicles, delivering collision and jackknifing avoidance at the expense of deadlocks and livelocks observed in 15,000 simulations.
A neural network trained on full-reference perceptual quality labels predicts minimal sufficient resolution for rendered video to enable power-efficient client-side rendering.
An optimistic confidence-interval ranking procedure for best-arm identification across multiple independent bandits yields lower average simple regret and error probability than prior methods when selecting high-performing agents for each game in GVGAI and Ludii.
Qualitative thematic analysis of the Adventure AI podcast identifies roles, successes, failures, and person-like treatment of AI in tabletop RPGs, providing a basis for appropriate AI integration in gaming.
Context-mediated domain adaptation treats user modifications to AI artifacts as implicit domain specifications that reshape LLM-powered multi-agent reasoning, demonstrated via the Seedentia system which extracted 46 domain knowledge entries from expert edits.
citing papers explorer
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Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes
Dream-Cubed releases a billion-scale voxel dataset and 3D diffusion models that generate controllable Minecraft worlds by operating directly on blocks.
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Change is Hard: Consistent Player Behavior Across Games with Conflicting Incentives
Players exhibit consistent flexibility or specialization behavior across two games with conflicting performance incentives, indicating individual agency dominates structural differences.
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PREVENT-JACK: Context Steering for Swarms of Long Heavy Articulated Vehicles
Prevent-Jack fuses six local behaviors into a context steering framework for swarms of heavy articulated vehicles, delivering collision and jackknifing avoidance at the expense of deadlocks and livelocks observed in 15,000 simulations.
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Seeing enough: non-reference perceptual resolution selection for power-efficient client-side rendering
A neural network trained on full-reference perceptual quality labels predicts minimal sufficient resolution for rendered video to enable power-efficient client-side rendering.
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Best Agent Identification for General Game Playing
An optimistic confidence-interval ranking procedure for best-arm identification across multiple independent bandits yields lower average simple regret and error probability than prior methods when selecting high-performing agents for each game in GVGAI and Ludii.
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Co-Creativity at the Table: A Qualitative Analysis of Creative Interactions in the Podcast "Adventure AI"
Qualitative thematic analysis of the Adventure AI podcast identifies roles, successes, failures, and person-like treatment of AI in tabletop RPGs, providing a basis for appropriate AI integration in gaming.
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Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems
Context-mediated domain adaptation treats user modifications to AI artifacts as implicit domain specifications that reshape LLM-powered multi-agent reasoning, demonstrated via the Seedentia system which extracted 46 domain knowledge entries from expert edits.