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cs.CL2026
PersonaVLM: Long-Term Personalized Multimodal LLMs
Chang Nie, Chaoyou Fu, Yifan Zhang +2
Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. P…
cs.CL2026
MedMT-Bench: Can LLMs Memorize and Understand Long Multi-Turn Conversations in Medical Scenarios?
Lin Yang, Yuancheng Yang, Xu Wang +2
Large Language Models (LLMs) have demonstrated impressive capabilities across various specialist domains and have been integrated into high-stakes areas such as medicine. However,…
cs.CL2026
ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following
Yuancheng Yang, Lin Yang, Xu Wang +2
As applications of large language models (LLMs) become increasingly complex, the demand for robust complex instruction following capabilities is growing accordingly. We argue that…