8 papers
Tool-Adaptive LLM Reranker
Zichuan Liu, Ruijin Hua
Generative Large Language Models (LLMs) have revolutionized information retrieval, yet their strictly parametric nature frequently leads to severe factual hallucinations when confr…
Sample-efficient LLM Optimization with Reset Replay
Zichuan Liu, Jinyu Wang, Lei Song +1
Recent advancements in LLM post-training, particularly through reinforcement learning and preference optimization, are key to boosting their reasoning capabilities. However, these…
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback
Yiyuan Yang, Zichuan Liu, Lei Song +6
Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanatory reasoning required for trans…
Diversified Scaling Inference in Time Series Foundation Models
Ruijin Hua, Zichuan Liu, Kun Zhang +1
The advancement of Time Series Foundation Models (TSFMs) has been driven primarily by large-scale pre-training, but inference-time compute potential remains largely untapped. This…
On the Effect of Sampling Diversity in Scaling LLM Inference
Tianchun Wang, Zichuan Liu, Yuanzhou Chen +5
Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it. Motivated by the observed…
Solving Hidden Monotone Variational Inequalities with Surrogate Losses
Ryan D'Orazio, Danilo Vucetic, Zichu Liu +3
Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minimizing projected Bellman error and min-ma…