3 citations · 3 across the 4 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.CL2026
On the Token Value Inequality in Efficient Reasoning
Runjia Zeng, Hang Hua, Yiyang Liu +5
Chain-of-Thought reasoning has enabled large language models to achieve substantial performance gains on complex tasks. However, these gains come at the cost of dramatically increa…
quant-ph2026
Q-Bridge: Code Translation for Quantum Machine Learning via LLMs
Runjia Zeng, Priyabrata Senapati, Ruixiang Tang +2
Large language models have recently shown potential in bridging the gap between classical machine learning and quantum machine learning. However, the lack of standardized, high-qua…
cs.CL2026
TokenSeek: Memory Efficient Fine Tuning via Instance-Aware Token Ditching
Runjia Zeng, Qifan Wang, Qiang Guan +6
Fine tuning has been regarded as a de facto approach for adapting large language models (LLMs) to downstream tasks, but the high training memory consumption inherited from LLMs mak…