MoSEs: Uncertainty-Aware AI-Generated Text Detection via Mixture of Stylistics Experts with Conditional Thresholds

• URL: http://arxiv.org/abs/2509.02499v3
• Date: Mon, 08 Sep 2025 02:08:49 GMT
• Title: MoSEs: Uncertainty-Aware AI-Generated Text Detection via Mixture of Stylistics Experts with Conditional Thresholds
• Abstract summary: We propose the Mixture of Stylistic Experts (MoSEs) framework that enables stylistics-aware quantification uncertainty.<n>MoSEs contains three core components, namely, the Stylistics Reference Repository (SRR), the Stylistics-Aware Router (SAR), and the Conditional Threshold Estimator (CTE)<n>With a discrimination score, MoSEs yields prediction labels with the corresponding confidence level.
• Score: 50.94175872742232
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of large language models has intensified public concerns about the potential misuse. Therefore, it is important to build trustworthy AI-generated text detection systems. Existing methods neglect stylistic modeling and mostly rely on static thresholds, which greatly limits the detection performance. In this paper, we propose the Mixture of Stylistic Experts (MoSEs) framework that enables stylistics-aware uncertainty quantification through conditional threshold estimation. MoSEs contain three core components, namely, the Stylistics Reference Repository (SRR), the Stylistics-Aware Router (SAR), and the Conditional Threshold Estimator (CTE). For input text, SRR can activate the appropriate reference data in SRR and provide them to CTE. Subsequently, CTE jointly models the linguistic statistical properties and semantic features to dynamically determine the optimal threshold. With a discrimination score, MoSEs yields prediction labels with the corresponding confidence level. Our framework achieves an average improvement 11.34% in detection performance compared to baselines. More inspiringly, MoSEs shows a more evident improvement 39.15% in the low-resource case. Our code is available at https://github.com/creator-xi/MoSEs.

Related papers

• MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models [70.34265674686516]
  Multimodal embedding models aim to map heterogeneous inputs, such as text, images, videos, and audio, into a shared semantic space.<n>We introduce MMEB-V3, a benchmark that evaluates embeddings across text, image, video, audio, as well as agent-centric scenarios.<n>We conduct a systematic analysis of full-modality embeddings and identify three key findings.
  arXiv  Detail & Related papers  (2026-04-25T14:15:05Z)
• CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling [61.75914342638658]
  CDRRM (Contrast-Driven Reward Model) is a framework for high-quality rubric generation and guided preference judgment.<n>This work offers a scalable, interpretable, and data-efficient path for reward modeling.
  arXiv  Detail & Related papers  (2026-03-09T07:15:23Z)
• Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models [15.709482146201283]
  A simple linear classifier trained on the frozen features of modern Vision Foundation Models establishes a new state-of-the-art.<n>We show that this baseline matches specialized detectors on standard benchmarks but also decisively outperforms them on in-the-wild datasets.<n>We conclude by advocating for a paradigm shift in AI forensics, moving from overfitting on static benchmarks to harnessing the evolving world knowledge of foundation models for real-world reliability.
  arXiv  Detail & Related papers  (2026-02-02T07:20:02Z)
• AI Generated Text Detection [0.0]
  This paper presents an evaluation of AI text detection methods, including both traditional machine learning models and transformer-based architectures.<n>We utilize two datasets, HC3 and DAIGT v2, to build a unified benchmark and apply a topic-based data split to prevent information leakage.<n>Results indicate that contextual modeling is significantly superior to lexical features and highlight the importance of mitigating topic memorization.
  arXiv  Detail & Related papers  (2026-01-07T11:18:10Z)
• Token-Level Marginalization for Multi-Label LLM Classifiers [0.0]
  Three novel token-level probability estimation approaches are proposed.<n>The aim is to enhance model interpretability and accuracy, and evaluate the generalizability of this framework across different instruction-tuned models.
  arXiv  Detail & Related papers  (2025-11-27T10:43:26Z)
• Opening the Black Box: Interpretable LLMs via Semantic Resonance Architecture [0.0]
  Mixture-of-Experts (MoE) models improve efficiency through sparse activation, yet typically rely on opaque, learned gating functions.<n>We introduce the Semantic Resonance Architecture (SRA), an MoE approach designed to ensure that routing decisions are inherently interpretable.<n>SRA replaces learned gating with a Chamber of Semantic Resonance (CSR) module, which routes tokens based on cosine similarity with trainable semantic anchors.
  arXiv  Detail & Related papers  (2025-09-12T09:02:48Z)
• Joint Learning using Mixture-of-Expert-Based Representation for Enhanced Speech Generation and Robust Emotion Recognition [54.44798086835314]
  Speech emotion recognition (SER) plays a critical role in building emotion-aware speech systems, but its performance degrades significantly under noisy conditions.<n>We propose the Sparse Mixture-of-Experts Representation Integration Technique (Sparse MERIT), a flexible MTL framework that applies frame-wise expert routing over self-supervised speech representations.<n> Experiments on the MSP-Podcast corpus show that Sparse MERIT consistently outperforms baseline models on both SER and SE tasks.
  arXiv  Detail & Related papers  (2025-09-10T10:18:56Z)
• RoHOI: Robustness Benchmark for Human-Object Interaction Detection [78.18946529195254]
  Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on the HICO-DET and V-COCO datasets and a new robustness-focused metric.
  arXiv  Detail & Related papers  (2025-07-12T01:58:04Z)
• AGENT-X: Adaptive Guideline-based Expert Network for Threshold-free AI-generated teXt detection [44.66668435489055]
  AGENT-X is a zero-shot multi-agent framework for AI-generated text detection.<n>We organize detection guidelines into semantic, stylistic, and structural dimensions, each independently evaluated by specialized linguistic agents.<n>A meta agent integrates these assessments through confidence-aware aggregation, enabling threshold-free, interpretable classification.<n>Experiments on diverse datasets demonstrate that AGENT-X substantially surpasses state-of-the-art supervised and zero-shot approaches in accuracy, interpretability, and generalization.
  arXiv  Detail & Related papers  (2025-05-21T08:39:18Z)
• Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition [58.98052764581606]
  We develop a novel uncertainty-Aware label refineMent on hYpergraphs (AMY) method.<n>For local training, each local model consists of a backbone, an uncertainty estimation (UE) block, and an expression classification (EC) block.<n>A personalized uncertainty estimator is then introduced to estimate reliable uncertainty weights of samples in the local client.
  arXiv  Detail & Related papers  (2025-01-03T13:59:21Z)
• Erasing Conceptual Knowledge from Language Models [24.63143961814566]
  We introduce Erasure of Language Memory (ELM), a principled approach to concept-level unlearning.<n>ELM operates by matching distributions defined by the model's own introspective classification capabilities.<n>We demonstrate ELM's efficacy on biosecurity, cybersecurity, and literary domain erasure tasks.
  arXiv  Detail & Related papers  (2024-10-03T17:59:30Z)
• Benchmark Granularity and Model Robustness for Image-Text Retrieval [44.045767657945895]
  We show how dataset granularity and query perturbations affect retrieval performance and robustness.<n>We show that richer captions consistently enhance retrieval, especially in text-to-image tasks.<n>Our results highlight variation in model robustness and a dataset-dependent relationship between caption granularity and sensitivity perturbation.
  arXiv  Detail & Related papers  (2024-07-21T18:08:44Z)
• UATVR: Uncertainty-Adaptive Text-Video Retrieval [90.8952122146241]
  A common practice is to transfer text-video pairs to the same embedding space and craft cross-modal interactions with certain entities. We propose an Uncertainty-language Text-Video Retrieval approach, termed UATVR, which models each look-up as a distribution matching procedure.
  arXiv  Detail & Related papers  (2023-01-16T08:43:17Z)
• Benchmarking the Robustness of LiDAR Semantic Segmentation Models [78.6597530416523]
  In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. We propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. We design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications.
  arXiv  Detail & Related papers  (2023-01-03T06:47:31Z)
• Improving CTC-based ASR Models with Gated Interlayer Collaboration [9.930655347717932]
  We present a Gated Interlayer Collaboration mechanism which introduces contextual information into the models. We train the model with intermediate CTC losses calculated by the interlayer outputs of the model, in which the probability distributions of the intermediate layers naturally serve as soft label sequences.
  arXiv  Detail & Related papers  (2022-05-25T03:21:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.