6 papers
Verifiable Self-Evolution for Open-Ended Dialogue Skills via Future-Feedback Prediction
ChaoJin Zhao, Xuan Jiang
Textual skills provide a lightweight way to improve frozen language-model agents, but their self-evolution normally requires a stable validation signal. Such signals are natural in…
Less is MoE: Trimming Experts in Domain-Specialist Language Models
Haoze He, Xinkai Zou, Xuan Jiang +4
Mixture-of-Experts (MoE) models achieve strong performance through conditional computation, but their large parameter footprint poses deployment challenges. Prior MoE compression a…
RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
Yuhan Tang, Kangxin Cui, Jung Ho Park +6
Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matc…
Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning
Haoze He, Xingyuan Ding, Xuan Jiang +5
Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layers are fragile. Methods such as DenseMix…
Towards Generalizable Context-aware Anomaly Detection: A Large-scale Benchmark in Cloud Environments
Xinkai Zou, Xuan Jiang, Ruikai Huang +8
Anomaly detection in cloud environments remains both critical and challenging. Existing context-level benchmarks typically focus on either metrics or logs and often lack reliable a…
Sparse Matrix in Large Language Model Fine-tuning
Haoze He, Juncheng Billy Li, Xuan Jiang +1
LoRA and its variants have become popular parameter-efficient fine-tuning (PEFT) methods due to their ability to avoid excessive computational costs. However, an accuracy gap often…