6 papers
Compositional Monte Carlo Tree Diffusion for Extendable Planning
Jaesik Yoon, Hyeonseo Cho, Sungjin Ahn
Monte Carlo Tree Diffusion (MCTD) integrates diffusion models with structured tree search to enable effective trajectory exploration through stepwise reasoning. However, MCTD remai…
CrafterDojo: A Suite of Foundation Models for Building Open-Ended Embodied Agents in Crafter
Junyeong Park, Hyeonseo Cho, Sungjin Ahn
Developing general-purpose embodied agents is a core challenge in AI. Minecraft provides rich complexity and internet-scale data, but its slow speed and engineering overhead make i…
Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning
Jaesik Yoon, Hyeonseo Cho, Yoshua Bengio +1
Diffusion models have recently emerged as a powerful approach for trajectory planning. However, their inherently non-sequential nature limits their effectiveness in long-horizon re…
Adaptive Inference-Time Scaling via Cyclic Diffusion Search
Gyubin Lee, Truong Nhat Nguyen Bao, Jaesik Yoon +4
Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. However, most inference-time scaling metho…
Monte Carlo Tree Diffusion for System 2 Planning
Jaesik Yoon, Hyeonseo Cho, Doojin Baek +2
Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)-whose performance naturally improves with inference-time comp…
MrSteve: Instruction-Following Agents in Minecraft with What-Where-When Memory
Junyeong Park, Junmo Cho, Sungjin Ahn
Significant advances have been made in developing general-purpose embodied AI in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches. While th…