10 citations · 13 across the 11 of their papers we have counts for
11 papers
From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation
Tianle Gu, Kexin Huang, Lingyu Li +6
Safety moderation is pivotal for identifying harmful content. Despite the success of textual safety moderation, its multimodal counterparts remain hindered by a dual sparsity of da…
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…
Dr. Bench: A Multidimensional Evaluation for Deep Research Agents, from Answers to Reports
Yang Yao, Yixu Wang, Yuxuan Zhang +9
As an embodiment of intelligence evolution toward interconnected architectures, Deep Research Agents (DRAs) systematically exhibit the capabilities in task decomposition, cross-sou…
LinguaSafe: A Comprehensive Multilingual Safety Benchmark for Large Language Models
Zhiyuan Ning, Tianle Gu, Jiaxin Song +8
The widespread adoption and increasing prominence of large language models (LLMs) in global technologies necessitate a rigorous focus on ensuring their safety across a diverse rang…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…
Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?
Yang Yao, Lingyu Li, Jiaxin Song +8
As Multimodal Large Language Models (MLLMs) continue to evolve, their cognitive and reasoning capabilities have seen remarkable progress. However, challenges in visual fine-grained…
HoneypotNet: Backdoor Attacks Against Model Extraction
Yixu Wang, Tianle Gu, Yan Teng +2
Model extraction attacks are one type of inference-time attacks that approximate the functionality and performance of a black-box victim model by launching a certain number of quer…
MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts
Tianle Gu, Kexin Huang, Ruilin Luo +4
Large Language Models (LLMs) can memorize sensitive information, raising concerns about potential misuse. LLM Unlearning, a post-hoc approach to remove this information from traine…
MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models
Tianle Gu, Zeyang Zhou, Kexin Huang +10
Powered by remarkable advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) demonstrate impressive capabilities in manifold tasks. However, the pra…
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models
Haiquan Zhao, Lingyu Li, Shisong Chen +11
Emotion Support Conversation (ESC) is a crucial application, which aims to reduce human stress, offer emotional guidance, and ultimately enhance human mental and physical well-bein…
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion
Ruilin Luo, Tianle Gu, Haoling Li +4
Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps by leveraging established temporal structural know…