arXiv (OAI Expanded)
open access
Valerant: An Automatic Navigable Game Map Generator via Action-Conditioned World Model Exploration
World Action Models (WAMs) couple predictive world modeling with action generation, allowing anticipated future states to guide agent behavior. Although WAMs are rapidly advancing embodied AI, general-purpose counterparts remain largely unexplored in games. Existing game-oriented approaches often co
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models#363299
- PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration#999828
- FluxVLA Engine: A One-Stop VLA Engineering Platform for Embodied Intelligence#1123592
- FluxVLA Engine: A One-Stop VLA Engineering Platform for Embodied Intelligence#919445
- CoMAP: Co-Evolving World Models and Agent Policies for LLM Agents#926060
Record · ID 1210086
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive