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20242026
most citedDetecting Training Data of Large Language Models via Expectation Maximization

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cs.CL2026

Change the Number, Change the Answer: Assessing Rating Scale Sensitivity in LLM-Based Text Scoring

Wonjeong Jo, Gyuwan Kim, Ming Shan Hee

Large language models (LLMs) are increasingly used in computational social science to assign scalar scores to social and psychological constructs. However, studies use different ra…

cs.CL2026

CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG

Gyuwan Kim, Cheoneum Park, Tao Yang

Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved contexts for higher efficiency, whi…

cs.CL2026

Global Optimization and Inference-Time Region Grafting for Agentic Workflows

Donghyeok Koh, Gyuwan Kim, Jinyeong Bak +4

Recent advances in agentic workflow optimization automate workflow design through task-specific workflow search or input-conditioned architecture selection. However, they determine…

cs.LG2026

KnapSpec: Self-Speculative Decoding via Adaptive Layer Selection as a Knapsack Problem

Seongjin Cha, Gyuwan Kim, Dongsu Han +2

Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often rely on static heuristics that ignore th…

cs.CL2026

PPA-Plan: Proactive Pitfall Avoidance for Reliable Planning in Long-Context LLM Reasoning

Byeongjin Kim, Gyuwan Kim, Seo Yeon Park

Large language models (LLMs) struggle with reasoning over long contexts where relevant information is sparsely distributed. Although plan-and-execute frameworks mitigate this by de…