most citedIster: Linear Transformer for Efficient Multivariate Time Series Forecasting

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.IR2026

LLM-Oriented Information Retrieval: A Denoising-First Perspective

Lu Dai, Liang Sun, Fanpu Cao +4

Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented generation (RAG) and agentic se…

cs.CV2026

When Looking Is Not Enough: Visual Attention Structure Reveals Hallucination in MLLMs

Fanpu Cao, Xin Zou, Xuming Hu +1

Multimodal large language models (MLLMs) have become a key interface for visual reasoning and grounded question answering, yet they remain vulnerable to visual hallucinations, wher…

cs.LG20261 cited

Ister: Linear Transformer for Efficient Multivariate Time Series Forecasting

Fanpu Cao, Shu Yang, Zhengjian Chen +2

Transformer-based models have achieved remarkable success in multivariate time series forecasting (MTSF) by capturing long-range dependencies. However, their widespread adoption is…

cs.LG2026

SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series Forecasting

Wenxuan Xie, Fanpu Cao

In recent work on time-series prediction, Transformers and even large language models have garnered significant attention due to their strong capabilities in sequence modeling. How…

cs.CV2026

ProCache: Constraint-Aware Feature Caching with Selective Computation for Diffusion Transformer Acceleration

Fanpu Cao, Yaofo Chen, Zeng You +1

Diffusion Transformers (DiTs) have achieved state-of-the-art performance in generative modeling, yet their high computational cost hinders real-time deployment. While feature cachi…

cs.LG2026

Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval

Fanpu Cao, Lu Dai, Jindong Han +1

Multivariate time series forecasting (MTSF) plays a vital role in numerous real-world applications, yet existing models remain constrained by their reliance on a limited historical…