3 papers
cs.SE2026
Tool Retrievers Are Underestimated: Annotation Expansion Reveals True Capability
Yanyu Zhu, Chenheng Zhang, Shaoshen Chen +8
In open-world scenarios with massive and evolving tool repositories, tool-augmented large language models rely on a retriever to surface relevant tools for a given query. Because s…
cs.AI2026
Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture
Hai Lin, Hoilam Pao, Shaoxiong Zhan +1
Large language models are undergoing a transition from model technology to system technology. Engineering challenges like cache reuse, context capacity, agent scheduling, and permi…
cs.CL2026
Experience-Driven Dynamic Exits for LLMs with Reinforcement Learning
Yanyu Zhu, Hoilam Pao, Niu Hu +6
Large Language Models suffer from slow autoregressive inference. While self-speculative decoding accelerates this process, its efficiency is hampered by static configurations like…