arXiv AI
Safety for Whom? Benchmarking Understanding Beyond Refusal in Egocentric Multimodal Assistants
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CASA: Classification Augmented with Safety Attention for Robust Multimodal Alignment
arXiv:2604. 00310v2 Announce Type: replace-cross Abstract: Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions.
arXiv AI
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EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards
arXiv:2607. 00218v1 Announce Type: cross Abstract: Vision-language models (VLMs) are now proposed as runtime safety guards for embodied agents in homes and factories.
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SafeAtlas-VL: Beyond Binary Multimodal Safety with Large-Scale Data and Guard Models
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arXiv AI
Jul 1
Harnessing Textual Refusal Directions for Multimodal Safety
arXiv:2606. 31876v1 Announce Type: new Abstract: To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space.
arXiv Computation and Language
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MME-Safety: A Fine-grained Benchmark for Safety Evaluation of MLLMs
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arXiv AI
Sep 10
AV-SafetyBench: A Safety Benchmark for Text-to-Audio-Video Generation
arXiv:2609.06991v1 Announce Type: cross Abstract: Recent text-to-audio-video (T2AV) models jointly generate video, speech, sound effects, and ambience from a single text prompt. This capability poses...