arXiv AI

Forewarned is Forearmed: When Non-Sequential Embedding Turns Into an Anomaly Detector

arXiv:2606. 30196v1 Announce Type: cross Abstract: This paper offers an in-depth analysis of non-sequential multimodal sentence-level embeddings, with a particular focus on the SONAR model.

arXiv AI

2d ago

Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings

arXiv:2507.12295v2 Announce Type: replace-cross Abstract: Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation id...

By Feng Xiao, Jicong Fan

llms rag nlp benchmarks

arXiv Machine Learning

Aug 17

Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments

arXiv:2608. 13652v1 Announce Type: new Abstract: Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity.

By Haoyi Jia, Sagar Addepalli, Julia Gonski

rag

arXiv AI

Jun 2

A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

arXiv:2606. 01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task.

By Stefano Samele, Eugenio Lomurno, Teodora Jovanovic, Sanjay Shivakumar Manohar, Alberto Crivellaro, Matteo Matteucci

llms rag multimodal benchmarks

arXiv AI

Jul 1

Listening Between the Lines: Joint Learning of ASR Embeddings and LLM-Augmented Linguistics for Dementia Detection

arXiv:2606. 30675v1 Announce Type: cross Abstract: Early detection of dementia through speech analysis offers a non-invasive screening alternative, but capturing both acoustic and linguistic biomarkers remains challenging.

By Olivier Jiyoun Jung, Jonghyeon Park, Myungwoo Oh

llms rag multimodal benchmarks

arXiv AI

Jul 15

The Sound of Absence: Audio-Language Embedding Models Struggle with Negation

arXiv:2607. 12290v1 Announce Type: cross Abstract: Audio-language embedding models such as CLAP are widely evaluated on matching present sound events, but rarely on negation.

By Chun-Yi Kuan, Hung-yi Lee

llms rag multimodal safety

Hugging Face Trending Papers

Jul 27

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the global contextual information encoded by Vision Transformers (ViTs) underexploited.

llms rag benchmarks

arXiv AI

Aug 5

From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model

arXiv:2508. 00955v3 Announce Type: replace-cross Abstract: Adapting generative Multimodal Large Language Models (MLLMs) into universal embedding models typically demands resource-intensive contrastive pre-training, while traditional hard negative mining methods suffer from severe false negative contamination.

By Yeong-Joon Ju, Seong-Whan Lee

llms rag fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 30

Same Concept, Different Directions: Cross-Modal Feature Heterogeneity in Sparse Autoencoders

arXiv:2606. 29888v1 Announce Type: new Abstract: Vision-language models map images and text into a joint embedding space.

By Chungpa Lee, Jihoon Kwon, Kyle Min, Jy-yong Sohn

llms rag multimodal safety

arXiv AI

Jul 28

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).

By Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang, Chih-Fan Hsu, Jeng-Lin Li

llms rag benchmarks

arXiv AI

Jul 7

Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings

arXiv:2607. 03806v1 Announce Type: cross Abstract: Audio foundation models are widely adopted as general-purpose feature extractors, yet the internal structure of their learned representations remains insufficiently understood.

By H\'ector Martel, Joe Hennessy-Priest, Taemin Cho

rag multimodal

arXiv AI

Jul 21

Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution

arXiv:2607. 17712v1 Announce Type: new Abstract: Detecting high-level semantic concepts like negation across modalities remains a challenge for current multimodal systems.

By Ali AbuSaleh, Leon Hammerla, Alexander Mehler

llms rag multimodal

arXiv AI

2d ago

What's the Catch? Evaluating Temporal Consistency in Vision-Language Models

The paper introduces TimeCatch, a benchmark that evaluates temporal consistency in vision‑language models (VLMs) by treating temporal grounding as an anomaly detection problem. Temporal anomalies are created by swapping consecutive frames, while frame‑level anomalies involve replacing a frame with Gaussian noise. Across synthetic and real‑world datasets, VLMs reliably detect and localize frame‑level anomalies but perform near chance on temporal anomaly detection, whereas humans excel at both tasks.

By Marek Hradil, Danae S\'anchez Villegas

llms multimodal benchmarks

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