1 citations · 1 across the 10 of their papers we have counts for
11 papers
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…
CSV-Decode: Certifiable Sub-Vocabulary Decoding for Efficient Large Language Model Inference
Dong Liu, Shu Wang, Yanxuan Yu +2
Large language models face significant computational bottlenecks during inference due to the expensive output layer computation over large vocabularies. We present CSV-Decode, a no…
TS-PEFT: Unveiling Token-Level Redundancy in Parameter-Efficient Fine-Tuning
Dabiao Ma, Ziming Dai, Zhimin Xin +3
Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate under an implicit assumption: Once a target module is selected, every token passing through it contributes…
Latent Thought Models with Variational Bayes Inference-Time Computation
Deqian Kong, Minglu Zhao, Dehong Xu +8
We propose a novel class of language models, Latent Thought Models (LTMs), which incorporate explicit latent thought vectors that follow an explicit prior model in latent space. Th…
Explore the Reasoning Capability of LLMs in the Chess Testbed
Shu Wang, Lei Ji, Renxi Wang +4
Reasoning is a central capability of human intelligence. In recent years, with the advent of large-scale datasets, pretrained large language models have emerged with new capabiliti…
ToolGen: Unified Tool Retrieval and Calling via Generation
Renxi Wang, Xudong Han, Lei Ji +3
As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation. Traditional method…
Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing
Han Jiang, Xiaoyuan Yi, Zhihua Wei +3
Warning: Contains harmful model outputs. Despite significant advancements, the propensity of Large Language Models (LLMs) to generate harmful and unethical content poses critical c…