Yo-Sub Han

SeqWM introduces a sequential behavioral watermarking framework that embeds ownership signals into history-conditioned transition patterns of LLM agent actions, providing robust and position-agnostic provenance tracking.

STAB is a novel specification-driven pipeline that generates test cases exposing algorithmic bottlenecks by combining constraint-bound maximization and adversarial structure injection, significantly improving bottleneck detection rates across various LLMs.

The paper introduces DLM-SWAI, a training-free method that effectively steers diffusion language models (DLMs) toward desired textual styles or properties by biasing the token distribution at each denoising step.

The paper proposes EPIC, an efficient and parallel decoding framework that significantly speeds up the process of constraining diffusion language model outputs using Context-Free Grammars (CFG).

The paper introduces LUNA, a linguistically adaptive watermarking technique that achieves high detection accuracy across diverse languages while maintaining minimal text distortion, outperforming existing methods significantly.