4 papers
Two Clocks in Diffusion MLLMs: When Answers Stabilize Before Rationales Unfold
Keuntae Kim, Yong Suk Choi
An answer candidate in a masked diffusion MLLM can stabilize while its rationale is still unfolding. We distinguish retrospective stabilization of the logged candidate from token c…
ST-Veto: Spatio-Temporal Token Veto for Diffusion MLLMs via Taylor Prediction and Visual Grounding
Keuntae Kim, Beomseok Lee, Hyunwoo Kim +1
Vision Language Models (VLMs) achieve strong reasoning with Chain-of-Thought (CoT) prompting but incur high sequential-generation cost, error accumulation, and limited self-correct…
Thinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Models
Keuntae Kim, Mingyu Kang, Yong Suk Choi
Diffusion large language models (dLLMs) are emerging as promising alternatives to autoregressive (AR) LLMs. Recently, this paradigm has been extended to multimodal tasks, leading t…
CLAWS:Creativity detection for LLM-generated solutions using Attention Window of Sections
Keuntae Kim, Eunhye Jeong, Sehyeon Lee +2
Recent advances in enhancing the reasoning ability of large language models (LLMs) have been remarkably successful. LLMs trained with reinforcement learning (RL) for reasoning demo…