2 citations · 4 across the 33 of their papers we have counts for
7 papers · 2 filters
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…
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…
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…
URECA: The Chain of Two Minimum Set Cover Problems exists behind Adaptation to Shifts in Semantic Code Search
Seok-Ung Choi, Joonghyuk Hahn, Yo-Sub Han
Adaptation is to make model learn the patterns shifted from the training distribution. In general, this adaptation is formulated as the minimum entropy problem. However, the minimu…
Detection of LLM-Paraphrased Code and Identification of the Responsible LLM Using Coding Style Features
Shinwoo Park, Hyundong Jin, Jeong-won Cha +1
Recent progress in large language models (LLMs) for code generation has raised serious concerns about intellectual property protection. Malicious users can exploit LLMs to produce…