2 citations · 4 across the 33 of their papers we have counts for
37 papers
CT-Miner: Fast and Coarse-Grained Time-Series Pattern Mining via Cartesian Trees
Hyundong Jin, Hyunki Hong, Yo-Sub Han
Time series often contain recurring structural patterns, and efficiently mining such patterns into compact representations is essential for scalable analysis of long sequences. Car…
Grammar-Guided Code Watermarking with Green Temperature
Hyundong Jin, Hyeseon An, Soohan Lim +1
Large language model watermarking embeds detectable statistical signals during decoding, but the resulting changes to token probabilities can degrade generation quality. This trade…
SEW: Style-Encoded Watermarking of LLM-Generated Code
Soohan Lim, Hyundong Jin, Yo-Sub Han
Code watermarking supports provenance tracking for code generated by LLMs. Modifying token selection to embed watermarks as an LLM generates code can create a trade-off between det…
SLICE: Specification-Level Isolation of Contract Enforcement
Soohan Lim, Hyundong Jin, Yo-Sub Han
Programming problems commonly specify both the computation a function should perform and the conditions that its inputs must satisfy. Large language models are widely used to gener…
One Adapter Pair per Model: A Universal Activation Interface for Language Models
Su-Hyeon Kim, Jiwan Mun, Yo-Sub Han
Activation-based tools are usually tied to one model's native hidden space, requiring probes, sparse autoencoders, and natural-language interpreters to be rebuilt or rediscovered f…
Linguistics-Aware Non-Distortionary LLM Watermarking
Shinwoo Park, Hyejin Park, Hyeseon An +1
Watermarking should identify language-model output without degrading quality or limiting verification to the model provider. Multilingual deployment makes this harder because morph…
EPIC: Efficient and Parallel Inference under CFG Constraints for Diffusion Language Models
Hyundong Jin, Yo-Sub Han
Controlling language model outputs is essential for ensuring structural validity, reliability, and downstream usability, and diffusion language models are no exception. Recent adva…
DLM-SWAI: Steering Diffusion Language Models Before They Unmask
Hyeseon An, Yo-Sub Han
Steering language model generation toward desired textual properties is essential for practical deployment, and inference-time methods are particularly appealing because they enabl…
STAB: Specification-driven Testing for Algorithmic Bottlenecks
Soohan Lim, Joonghyuk Hahn, Hyundong Jin +1
Evaluating the efficiency of algorithmic code requires test cases that expose runtime bottlenecks. Previous methods generate efficiency test cases either by increasing input size o…
Adaptive Steering and Remasking for Safe Generation in Diffusion Language Models
Yejin Lee, Yo-Sub Han, Ungsik Kim
Diffusion Language Models(DLMs) provide a promising alternative to autoregressive language models through iterative denoising and bidirectional generation. However, their iterative…
Sequential Behavioral Watermarking for LLM Agents
Hyeseon An, Shinwoo Park, Dongsu Kim +1
LLM-based agents act through sequences of executable decisions, but their trajectories provide little evidence of which agent or policy produced them, making provenance, ownership,…
NCO: A Versatile Plug-in for Handling Negative Constraints in Decoding
Hyundong Jin, Yo-Sub Han
Controlling Large Language Models (LLMs) to prevent the generation of undesirable content, such as profanity and personally identifiable information (PII), has become increasingly…
Cross-Family Universality of Behavioral Axes via Anchor-Projected Representations
Su-Hyeon Kim, Yo-Sub Han
Large language models from different families use different hidden dimensions, tokenizers, and training procedures, making behavioral directions difficult to compare or transfer ac…
Toward E2E Intelligence in 6G Networks: An AI Agent-Based RAN-CN Converged Intelligence Framework
Youbin Han, Haneul Ko, Namseok Ko +2
Recent advances in intelligent network control have primarily relied on task-specific Artificial Intelligence (AI) models deployed separately within the Radio Access Network (RAN)…
How Does the Thinking Step Influence Model Safety? An Entropy-based Safety Reminder for LRMs
Su-Hyeon Kim, Hyundong Jin, Yejin Lee +1
Large Reasoning Models (LRMs) achieve remarkable success through explicit thinking steps, yet the thinking steps introduce a novel risk by potentially amplifying unsafe behaviors.…
When LLM Essays Outscore Student Essays: What a Korean Writing Rubric Rewards and Where Readers Disagree
Shinwoo Park, Yo-Sub Han
LLMs now help students plan, draft, and revise essays. Educational assessment therefore faces a basic question: how should student and LLM writing be compared? Rubrics assign point…
Steering Language Models Before They Speak: Logit-Level Interventions
Hyeseon An, Shinwoo Park, Hyundong Jin +1
Controllable generation requires language models to realize output characteristics such as reading level, politeness, and toxicity. Existing steering methods are often indirect, re…
WaterMod: Modular Token-Rank Partitioning for Probability-Balanced LLM Watermarking
Shinwoo Park, Hyejin Park, Hyeseon Ahn +1
Large language models now draft news, legal analyses, and software code with human-level fluency. At the same time, regulations such as the EU AI Act mandate that each synthetic pa…
ECO: Enhanced Code Optimization via Performance-Aware Prompting for Code-LLMs
Su-Hyeon Kim, Joonghyuk Hahn, Sooyoung Cha +1
Code runtime optimization-the task of rewriting a given code to a faster one-remains challenging, as it requires reasoning about performance trade-offs involving algorithmic and st…
RegexPSPACE: A Benchmark for Evaluating LLM Reasoning on PSPACE-complete Regex Problems
Hyundong Jin, Joonghyuk Hahn, Yo-Sub Han
Large language models (LLMs) show strong performance across natural language processing (NLP), mathematical reasoning, and programming, and recent large reasoning models (LRMs) fur…
MECO: Multi-Expert Consensus for Code Time Complexity Prediction
Joonghyuk Hahn, Soohan Lim, Yo-Sub Han
Predicting the complexity of source code is essential for software development and algorithm analysis. Recently, Baik et al. (2025) introduced CodeComplex for code time complexity…
Repairing Regex Vulnerabilities via Localization-Guided Instructions
Sicheol Sung, Joonghyuk Hahn, Yo-Sub Han
Regular expressions (regexes) are foundational to modern computing for critical tasks like input validation and data parsing, yet their ubiquity exposes systems to regular expressi…
A Linguistics-Aware LLM Watermarking via Syntactic Predictability
Shinwoo Park, Hyejin Park, Hyeseon An +1
As large language models (LLMs) continue to advance rapidly, reliable governance tools have become critical. Publicly verifiable watermarking is particularly essential for fosterin…
ContractEval: A Benchmark for Evaluating Contract-Satisfying Assertions in Code Generation
Soohan Lim, Joonghyuk Hahn, Hyunwoo Park +2
Current code generation evaluation measures functional correctness on well-formed inputs that satisfy all input preconditions. This paradigm has a critical limitation: task descrip…