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20232026
most citedTEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

36 citations · 38 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Edit, Don't Imitate: Minimal-Edit On-Policy Distillation for Multimodal Reasoning

Wen Ye, Ailin Deng, Zirui Wang +8

Visual mathematical reasoning can fail before reasoning begins: when a vision-language model (VLM) misreads a diagram, subsequent reasoning proceeds from false premises. This perce…

cs.LG2026

Write Back the : Revisiting the Same Tokens with Fresh Representations

Wencheng Ye, Anning Hu, Xiangdong Zhang +5

Transformers process information strictly forward through depth, preventing deeper computation from revisiting and refining earlier representations. To augment the standard forward…

cs.LG2025

When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference

Wen Ye, Jinbo Liu, Defu Cao +2

The rapid advancement of Large Language Models (LLMs) has sparked growing interest in their application to time series analysis tasks. However, their ability to perform complex rea…

cs.LG2024

TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis

Wen Ye, Wei Yang, Defu Cao +4

Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to eith…

cs.LG2024★ 2 cited

TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model

Defu Cao, Wen Ye, Yizhou Zhang +1

Foundation models, particularly Large Language Models (LLMs), have revolutionized text and video processing, yet time series data presents distinct challenges for such approaches d…

cs.LG2023★ 36 cited

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Defu Cao, Furong Jia, Sercan O Arik +4

The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary high…