# SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection

arXiv: https://arxiv.org/abs/2609.08200 · HTML: https://mlsanity.com/paper/2609.08200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 42 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Deep Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/1911.08623/) (2019)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [Revisiting Over-smoothing in BERT from the Perspective of Graph](https://mlsanity.com/paper/2202.08625/) (2022)
- [LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks](https://mlsanity.com/paper/2112.05355/) (2021)
- [Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2207.12201/) (2022)
- [DATE: Detecting Anomalies in Text via Self-Supervision of Transformers](https://mlsanity.com/paper/2104.05591/) (2021)
- [PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection](https://mlsanity.com/paper/2310.11676/) (2023)
- [NLP-ADBench: NLP Anomaly Detection Benchmark](https://mlsanity.com/paper/2412.04784/) (2024)
- [DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning](https://mlsanity.com/paper/2603.16459/) (2026)
- [OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models](https://mlsanity.com/paper/2601.12996/) (2026)
- [From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach](https://mlsanity.com/paper/2202.05525/) (2022)
- [Few-shot Anomaly Detection in Text with Deviation Learning](https://mlsanity.com/paper/2308.11780/) (2023)
- [Anomaly Detection Based on Isolation Mechanisms: A Survey](https://mlsanity.com/paper/2403.10802/) (2024)
- [FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection](https://mlsanity.com/paper/2508.10594/) (2025)
- [Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling](https://mlsanity.com/paper/2502.10706/) (2025)
- [From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection](https://mlsanity.com/paper/2602.18793/) (2026)
- [Influence-Oriented Personalized Federated Learning](https://mlsanity.com/paper/2410.03315/) (2024)
- [TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection](https://mlsanity.com/paper/2501.11960/) (2025)
- [Towards One-for-All Anomaly Detection for Tabular Data](https://mlsanity.com/paper/2603.14407/) (2026)
- [FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection](https://mlsanity.com/paper/2605.09428/) (2026)
- [Multi-Stage Verification-Centric Framework for Mitigating Hallucination in Multi-Modal RAG](https://mlsanity.com/paper/2507.20136/) (2025)
- [Text Anomaly Detection with Simplified Isolation Kernel](https://mlsanity.com/paper/2510.13197/) (2025)
- [Towards Anomaly Detection on Relational Data](https://mlsanity.com/paper/2606.18621/) (2026)
- [Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach](https://mlsanity.com/paper/2605.25429/) (2026)
- [Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations](https://mlsanity.com/paper/2601.17786/) (2026)

## Abstract (arXiv)

Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-grained document-level detection by localizing anomalous tokens within text. By providing fine-grained abnormality prediction, token-level text anomaly detection plays a critical role in various real-world applications, such as spam filtering and fake news detection. However, existing methods still rely on the global distance calculation for scoring, during which the local anomaly signals are severely diluted by numerous redundant normal feature dimensions. Moreover, pre-trained language models used in these methods inevitably smooth out surface anomalies, further limiting their effectiveness in token-level anomaly detection. To address these limitations, we propose a Subspace Interaction-based Method (SIM for short) for token-level text anomaly detection. To prevent local signal dilution, SIM adopts a subspace interaction-based anomaly detector, which decouples high-dimensional token embeddings into multiple low-dimensional ones, amplifying localized anomaly signals hidden within specific dimensions. To counteract the over-smoothing effect, we design a hard pseudo-anomaly generation module to construct pseudo-anomalous tokens, simulating the subtle anomalies obscured by semantic smoothing. Also, a probabilistic boundary loss is developed to standardize anomaly scores into statistical distances, effectively enforcing anomalous instances to deviate significantly from the normal distribution center. Extensive experiments on multiple benchmark datasets verify the effectiveness of SIM and demonstrate its remarkable efficiency, robustness, and interpretability. The source code is available at: https://github.com/yankehan/SIM-TAD.