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To Keep or Not to Keep: Learning KV Cache Retention in Disaggregated LLM Serving Systems
Dong Liu, Yanxuan Yu, Eric Jiang +2
Disaggregated \textsc{LLM} serving separates prefill and decode across node pools, placing a network fabric between \textsc{KV}-cache computation and consumption. This breaks a key…
RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification
Yanxuan Yu, Dong Liu, Shu Wang +9
Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class samples are scarce yet critical for…
Se-DPO: Self-Evolving Token Credit for Direct Preference Optimization
Wenxiao Zhao, Shu Wang, Ying Nian Wu
Direct Preference Optimization (DPO) aggregates token-level log-probability ratios via uniform summation, implicitly treating all tokens as contributing equally to the preference s…
A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
Wenxiao Zhao, Dong Liu, Kaiyi Xu +9
Symbolic regression aims to discover closed-form equations from data, but existing LLM-guided methods often rely on a unified proposal loop that compresses heterogeneous search fai…
MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents
Shu Wang, Edwin Yu, Oscar Love +4
Large Language Model (LLM) agents require persistent memory to maintain personalization, factual continuity, and long-horizon reasoning, yet standard context-window and retrieval-a…