activity
20232026
most citedFrom GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities

3 citations · 10 across the 29 of their papers we have counts for

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Showing 2026Show all

9 papers · 1 filter

cs.CL2026

OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution

Yunhao Chen, Xin Wang, Yixu Wang +6

AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model interactions, agent behavior…

cs.CR2026

BraveGuard: From Open-World Threats to Safer Computer-Use Agents

Yunhao Feng, Xiaohu Du, Xinhao Deng +13

Computer-use agents extend language models from text generation to sustained interaction with files, terminals, browsers, and external tools. This shift creates safety risks that a…

cs.CL2026

SentGuard: Sentence-Level Streaming Guardrails for Large Language Models

Jiaqi Yu, Xin Wang, Yixu Wang +4

Large language models increasingly stream long, reasoning-intensive responses in real time, making when to moderate as critical as whether to moderate. Existing guardrails fall int…

eess.AS2026

A Unified and Reproducible Experimentation Framework for Speech Understanding

Jing Peng, Junhao Du, Chenghao Wang +21

Speech foundation models and Speech LLMs have advanced speech understanding, yet deployment-oriented model selection is hindered by non-comparable evaluations caused by mismatched…

cs.CR2026

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

Xiao Li, Xiang Zheng, Yifeng Gao +35

Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As…

cs.CL2026

Towards Context-Invariant Safety Alignment for Large Language Models

Yixu Wang, Yang Yao, Xin Wang +4

Preference-based post-training aligns LLMs with human intent, yet safety behavior often remains brittle. A model may refuse a harmful request in a standard prompt but comply when t…

cs.AI2026

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

Xingjun Ma, Yixu Wang, Hengyuan Xu +18

The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has driven major gains in reasoning, perception, and generation across language and…

cs.CR2026

OpenRT: An Open-Source Red Teaming Framework for Multimodal LLMs

Xin Wang, Yunhao Chen, Juncheng Li +7

The rapid integration of Multimodal Large Language Models (MLLMs) into critical applications is increasingly hindered by persistent safety vulnerabilities. However, existing red-te…

cs.CL2026

When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life

Xinyue Lou, Jinan Xu, Jingyi Yin +8

As Multimodal Large Language Models (MLLMs) become an indispensable assistant in human life, the unsafe content generated by MLLMs poses a danger to human behavior, perpetually ove…