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20212026
most citedMultiDocFusion: Hierarchical and Multimodal Chunking Pipeline for Enhanced RAG on Long Industrial Documents

3 citations · 6 across the 21 of their papers we have counts for

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cs.AI2026

Vision Is Not Overhead: One-Pass Block Drafting for Lossless Speculative Decoding in Vision-Language Models

Jungseob Lee, Seongtae Hong, Dongyub Jude Lee +4

Speculative decoding accelerates generation without changing its output, yet on vision-language models (VLMs) it has been caught in a self-defeating cycle. The drafter stays autore…

cs.AI2026

Skin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations

Dongyub Jude Lee, Jungseob Lee, Seungyoon Lee +5

Alignment tuning is meant to make harmful-request refusal robust, yet this safety behavior can be erased by a small set of benign fine-tuning examples. This is a deployment risk fo…

cs.AI2026

DART: Draft-Agreement Routing for Training-Free Adaptive Thinking Budgets in Hybrid Reasoning Models

Jungseob Lee, Seongtae Hong, Seungjun Lee +7

Hybrid reasoning models can answer directly or spend extra tokens on extended thinking. A practical router should choose between these modes for each query, so easy problems avoid…

cs.AI2026

HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering

Joongmin Shin, Gyuho Shim, Jeongbae Park +2

Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottlenecks: routing failure in l…

cs.AI20263 cited

MultiDocFusion: Hierarchical and Multimodal Chunking Pipeline for Enhanced RAG on Long Industrial Documents

Joongmin Shin, Chanjun Park, Jeongbae Park +2

RAG-based QA has emerged as a powerful method for processing long industrial documents. However, conventional text chunking approaches often neglect complex and long industrial doc…

cs.AI2025

Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language Models

Hyeonseok Moon, Jaehyung Seo, Seungyoon Lee +2

One of the key strengths of Large Language Models (LLMs) is their ability to interact with humans by generating appropriate responses to given instructions. This ability, known as…