3 papers
cs.LG2026
Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models
Kun Feng, Shaocheng Lan, Yuchen Fang +6
Inherent temporal heterogeneity, such as varying sampling densities and periodic structures, has posed substantial challenges in zero-shot generalization for Time Series Foundation…
cs.LG2026
Linking Process to Outcome: Conditional Reward Modeling for LLM Reasoning
Zheng Zhang, Ziwei Shan, Kaitao Song +2
Process Reward Models (PRMs) have emerged as a promising approach to enhance the reasoning capabilities of large language models (LLMs) by guiding their step-by-step reasoning towa…
cs.LG2025
Learning to Select In-Context Demonstration Preferred by Large Language Model
Zheng Zhang, Shaocheng Lan, Lei Song +3
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks during inference using only a few demonstrations. However, ICL performance is highly dependent…