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cs.CL2026
Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning
Jingxiang Chen, Minseok Kim, Seong-Gyun Leem +13
Speech large language models (LLMs) observe paralinguistic cues such as prosody, emotion, and non-verbal sounds--crucial for intent understanding. However, leveraging these cues fa…
cs.CV2026
PixSearch: Region-Grounded Retrieval for Knowledgeable Large Multimodal Models
Jeonghwan Kim, Renjie Tao, Sanat Sharma +8
Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on modular pipelines that separate visual…
cs.CV2026
Intelligent Power Grid Design Review via Active Perception-Enabled Multimodal Large Language Models
Taoliang Tan, Chengwei Ma, Zhen Tian +3
The intelligent review of power grid engineering design drawings is crucial for power system safety. However, current automated systems struggle with ultra-high-resolution drawings…