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cs.CL2026

ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models

Jiahui Guang, Haiyan Wang, Yingjie Zhu +4

Multimodal large language models (MLLMs) may memorize sensitive cross-modal information during pretraining, making machine unlearning (MU) crucial. Existing methods typically evalu…

cs.CL2026

AEQ-Bench: Measuring Empathy of Omni-Modal Large Models

Xuan Luo, Lewei Yao, Libo Zhao +6

While the automatic evaluation of omni-modal large models (OLMs) is essential, assessing empathy remains a significant challenge due to its inherent affectivity. To investigate thi…

cs.CL2025

Step-DeepResearch Technical Report

Chen Hu, Haikuo Du, Heng Wang +64

As LLMs shift toward autonomous agents, Deep Research has emerged as a pivotal metric. However, existing academic benchmarks like BrowseComp often fail to meet real-world demands f…

cs.CL2025

Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction

Ailin Huang, Boyong Wu, Bruce Wang +142

Real-time speech interaction, serving as a fundamental interface for human-machine collaboration, holds immense potential. However, current open-source models face limitations such…

cs.CL2024

Unveiling the Impact of Multi-Modal Interactions on User Engagement: A Comprehensive Evaluation in AI-driven Conversations

Lichao Zhang, Jia Yu, Shuai Zhang +10

Large Language Models (LLMs) have significantly advanced user-bot interactions, enabling more complex and coherent dialogues. However, the prevalent text-only modality might not fu…