activity
20202026
most citedSelf-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs

39 citations · 63 across the 24 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Reason in the Words You Speak: Idiolectal Paraphrasing Off-Policy Traces for Reasoning Distillation in VideoLLMs

Ji Soo Lee, Jinyoung Park, Seohyun Lee +4

Recent large language models achieve strong performance on complex reasoning tasks, where reinforcement learning with Group Relative Policy Optimization (GRPO) has emerged as a lea…

cs.CV2026

Context Blindness in DPO: Mitigating Object Hallucination in MLLMs via Context-Calibrated Preference Optimization

Byungoh Ko, Jinyoung Park, Jongha Kim +3

Multimodal large language models (MLLMs) have made rapid progress, yet they still exhibit object hallucination, generating plausible but incorrect descriptions that are inconsisten…

cs.CV2026

RegFormer: Transferable Relational Grounding for Efficient Weakly-Supervised Human-Object Interaction Detection

Jihwan Park, Chanhyeong Yang, Jinyoung Park +2

Weakly-supervised Human-Object Interaction (HOI) detection is essential for scalable scene understanding, as it learns interactions from only image-level annotations. Due to the la…

cs.CV2026

MoE-GRPO: Optimizing Mixture-of-Experts via Reinforcement Learning in Vision-Language Models

Dohwan Ko, Jinyoung Park, Seoung Choi +3

Mixture-of-Experts (MoE) has emerged as an effective approach to reduce the computational overhead of Transformer architectures by sparsely activating a subset of parameters for ea…

cs.AI2026

Improving Large Molecular Language Model via Relation-aware Multimodal Collaboration

Jinyoung Park, Minseong Bae, Jeehye Na +1

Large language models (LLMs) have demonstrated their instruction-following capabilities and achieved powerful performance on various tasks. Inspired by their success, recent works…

cs.CL2026

HyperGraphPro: Progress-Aware Reinforcement Learning for Structure-Guided Hypergraph RAG

Jinyoung Park, Sanghyeok Lee, Omar Zia Khan +2

Graph Retrieval-Augmented Generation (GraphRAG) has emerged as a promising paradigm that organizes external knowledge into structured graphs of entities and relations, enabling lar…