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cs.LG2026
Explaining and Tuning Transformer-based LLMs in Arithmetic Tasks with Human Strategies
Luyu Qiu, Jianing Li, Hwanhee Kim +4
Transformer-based large language models (LLMs) continue to achieve state-of-the-art performance across various natural language processing tasks. However, their subpar performance…
cs.LG2026
Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
Ruihan Xu, Yuting Gao, Lan Wang +5
Large Multimodal Models (LMMs) have achieved remarkable success in vision-language tasks, yet their vast parameter counts are often underutilized during both training and inference…