Hate speech remains prevalent in human society and continues to evolve in its forms and expressions.Modern advancements in the internet and online anonymity accelerate its rapid spread and complicate its detection.However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each reflecting different linguistic styles and social contexts.Despite this diversity, prior studies on hate speech detection often rely on fixed methodologies without adapting to dataspecific features.We introduce RV-HATE, a detection framework designed to account for the dataset-specific characteristics of each hate speech dataset.RV-HATE consists of multiple specialized modules, where each module focuses on distinct linguistic or contextual features of hate speech.The framework employs reinforcement learning to optimize weights that determine the contribution of each module for a given dataset.A voting mechanism then aggregates the module outputs to produce the final decision.RV-HATE offers two primary advantages: (1) it improves detection accuracy by tailoring the detection process to dataset-specific attributes, and (2) it also provides interpretable insights into the distinctive features of each dataset.Consequently, our approach effectively addresses implicit hate speech and achieves superior performance compared to conventional static methods.Our code is available at https: //github.com/leeyejin1231/RV-HATE.

As large language models (LLMs) continue to advance rapidly, reliable governance tools have become critical.Publicly verifiable watermarking is particularly essential for fostering a trustworthy AI ecosystem.A central challenge persists: balancing text quality against detection robustness.Recent studies have sought to navigate this trade-off by leveraging signals from model output distributions (e.g., token-level entropy); however, their reliance on these modelspecific signals presents a significant barrier to public verification, as the detection process requires access to the logits of the underlying model.We introduce STELA, a novel framework that aligns watermark strength with the linguistic degrees of freedom inherent in language.STELA dynamically modulates the signal using part-of-speech (POS) n-gram-modeled linguistic indeterminacy, weakening it in grammatically constrained contexts to preserve quality and strengthening it in contexts with greater linguistic flexibility to enhance detectability.Our detector operates without access to any model logits, thus facilitating publicly verifiable detection.Through extensive experiments on typologically diverse languages-analytic English, isolating Chinese, and agglutinative Korean-we show that STELA surpasses prior methods in detection robustness.