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cs.AI2025
DART: Difficulty-Adaptive Reasoning Truncation for Efficient Large Language Models
Ruofan Zhang, Bin Xia, Zhen Cheng +4
Adaptive reasoning is essential for aligning the computational effort of large language models (LLMs) with the intrinsic difficulty of problems. Current chain-of-thought methods bo…
cs.IR2025
An item is worth one token in Multimodal Large Language Models-based Sequential Recommendation
Qiyong Zhong, Jiajie Su, Ming Yang +3
Sequential recommendations (SR) predict users' future interactions based on their historical behavior. The rise of Large Language Models (LLMs) has brought powerful generative and…
cs.LG2025
AROMA: Autonomous Rank-one Matrix Adaptation
Hao Nan Sheng, Zhi-yong Wang, Mingrui Yang +1
As large language models continue to grow in size, parameter-efficient fine-tuning (PEFT) has become increasingly crucial. While low-rank adaptation (LoRA) offers a solution throug…