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Sung Ju Hwang

ICLR 2026×5ACL 2026×2CVPR 2026×2多模态智能体与工具可解释性推理检索与知识
新星分资深
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方向广度16
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0 一作 · 9 篇 · ICLR 2026 / ACL 2026 / CVPR 2026

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2026 顶会论文

9
作者位次 #4/4ICLR 2026Poster· Applications / Language, Speech and Dialog

Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs

Yumin Choi · Dongki Kim · Jinheon Baek · Sung Ju Hwang

多模态OpenReview
作者位次 #3/3ICLR 2026Poster· General Machine Learning / Representation Learning

Multi-View Encoders for Performance Prediction in LLM-Based Agentic Workflows

Patara Trirat · Wonyong Jeong · Sung Ju Hwang

智能体与工具可解释性OpenReview
作者位次 #4/4ICLR 2026Poster· Applications / Language, Speech and Dialog

Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning

Minju Seo · Jinheon Baek · Seongyun Lee · Sung Ju Hwang

多模态代码OpenReview
作者位次 #5/5ACL 2026Poster· Natural Language Processing

U niversal RAG : Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities

Woongyeong Yeo · Kangsan Kim · Soyeong Jeong · Jinheon Baek · Sung Ju Hwang

摘要

Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. However, most existing approaches are limited to a text-only corpus, and while recent efforts have extended RAG to other modal...

检索与知识多模态可解释性OpenReview 项目页
作者位次 #3/5ACL 2026Findings· Findings of ACL

When Thoughts Meet Facts: Reusable Reasoning for Long-Context LM s

Soyeong Jeong · Taehee Jung · Sung Ju Hwang · Joo-Kyung Kim · Dongyeop Kang

摘要

Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning by integrating large sets of retrieved documents or, in some cases, directly all necessary information. However, simpl...

检索与知识推理强化学习与后训练OpenReview 项目页

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