1 citations · 3 across the 13 of their papers we have counts for
13 papers
AgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems
Jaewon Chu, Jinwoo Seo, Jaewon Cho +4
Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design o…
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…
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…
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…
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO
Jinyoung Park, Jeehye Na, Jinyoung Kim +1
Recent works have demonstrated the effectiveness of reinforcement learning (RL)-based post-training for enhancing the reasoning capabilities of large language models (LLMs). In par…
Latent Bayesian Optimization via Autoregressive Normalizing Flows
Seunghun Lee, Jinyoung Park, Jaewon Chu +2
Bayesian Optimization (BO) has been recognized for its effectiveness in optimizing expensive and complex objective functions. Recent advancements in Latent Bayesian Optimization (L…
Inversion-based Latent Bayesian Optimization
Jaewon Chu, Jinyoung Park, Seunghun Lee +1
Latent Bayesian optimization (LBO) approaches have successfully adopted Bayesian optimization over a continuous latent space by employing an encoder-decoder architecture to address…
Generative Subgraph Retrieval for Knowledge Graph-Grounded Dialog Generation
Jinyoung Park, Minseok Joo, Joo-Kyung Kim +1
Knowledge graph-grounded dialog generation requires retrieving a dialog-relevant subgraph from the given knowledge base graph and integrating it with the dialog history. Previous w…
Retrieval-Augmented Open-Vocabulary Object Detection
Jooyeon Kim, Eulrang Cho, Sehyung Kim +1
Open-vocabulary object detection (OVD) has been studied with Vision-Language Models (VLMs) to detect novel objects beyond the pre-trained categories. Previous approaches improve th…
Prompt Learning via Meta-Regularization
Jinyoung Park, Juyeon Ko, Hyunwoo J. Kim
Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently, prompt learning approaches have b…
Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection
Jongha Kim, Jihwan Park, Jinyoung Park +3
Visual Relationship Detection (VRD) has seen significant advancements with Transformer-based architectures recently. However, we identify two key limitations in a conventional labe…
Graph Elicitation for Guiding Multi-Step Reasoning in Large Language Models
Jinyoung Park, Ameen Patel, Omar Zia Khan +2
Chain-of-Thought (CoT) prompting along with sub-question generation and answering has enhanced multi-step reasoning capabilities of Large Language Models (LLMs). However, prompting…
Distribution-Aware Prompt Tuning for Vision-Language Models
Eulrang Cho, Jooyeon Kim, Hyunwoo J. Kim
Pre-trained vision-language models (VLMs) have shown impressive performance on various downstream tasks by utilizing knowledge learned from large data. In general, the performance…