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Yunyang Xuan

2 accepted papers

2026

Inference Scaling Law for Retrieval Augmented Generation

AAAI 2026technical

Retrieval-augmented generation (RAG) has recently emerged as a powerful framework for knowledge-intensive natural language processing tasks, which leverages the strengths of both pre-trained language models and external knowledge. While significant progress has been made, the scaling behavior of the

Cited by 0SourcePDFScholar
2025

MERIT: Multi-Agent Collaboration for Unsupervised Time Series Representation Learning

ACL 2025finding

This paper studies the problem of unsupervised time series representation learning, which aims to map unlabeled time series data into a low-dimensional latent space for various downstream tasks. Previous works usually combine a range of augmentation strategies with contrastive learning to generate d…

Cited by 0SourcePDFScholar