8 papers · 1 filter
Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement
Taegu Kang, Jaesik Yoon, Sungjin Ahn
Inference-time scaling has emerged as a major approach for improving reasoning capabilities, and has been increasingly applied to diffusion models. However, existing inference-time…
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…
Loopholing Discrete Diffusion: Deterministic Bypass of the Sampling Wall
Mingyu Jo, Jaesik Yoon, Justin Deschenaux +2
Discrete diffusion models offer a promising alternative to autoregressive generation through parallel decoding, but they suffer from a sampling wall: once categorical sampling occu…
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…
Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
Minsu Kim, Jean-Pierre Falet, Oliver E. Richardson +5
Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can contain inaccurate statements that reduce pe…
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…