activity
20242026
most citedPosition: What Can Large Language Models Tell Us about Time Series Analysis

6 citations · 13 across the 9 of their papers we have counts for

collaborators

9 papers

cs.MM2026

UniPath: Adaptive Coordination of Understanding and Generation for Unified Multimodal Reasoning

Hayes Bai, Yinyi Luo, Wenwen Wang +2

Unified multimodal models (UMMs) aim to integrate understanding and generation within a single architecture. However, it remains underexplored how to effectively coordinate these t…

cs.AI2026

Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory

Mingxi Zou, Zhihan Guo, Langzhang Liang +6

Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, sali…

cs.LG2025

The Procrustean Bed of Time Series: The Optimization Bias in Point-wise Loss Functions

Rongyao Cai, Yuxi Wan, Kexin Zhang +6

Intuitively, a more deterministic time series should be easier to forecast. However, point-wise loss functions (e.g., MSE and MAE), serving as differentiable surrogates for the ide…

cs.MA2025

Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems

Jiaxi Yang, Mengqi Zhang, Yiqiao Jin +8

Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. However, the topology of th…

cs.AI2025★ 2 cited

CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries

Shudong Liu, Yiqiao Jin, Cheng Li +6

Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding, often misinterpreting symbols, gestures, and artifacts due to biases in p…

cs.LG2024★ 1 cited

A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization

Haoxin Liu, Chenghao Liu, B. Aditya Prakash

Large language models (LLMs), with demonstrated reasoning abilities across multiple domains, are largely underexplored for time-series reasoning (TsR), which is ubiquitous in the r…

cs.LG2024★ 4 cited

Time Series Analysis for Education: Methods, Applications, and Future Directions

Shengzhong Mao, Chaoli Zhang, Yichi Song +4

Recent advancements in the collection and analysis of sequential educational data have brought time series analysis to a pivotal position in educational research, highlighting its…

cs.AI2024

CulturePark: Boosting Cross-cultural Understanding in Large Language Models

Cheng Li, Damien Teney, Linyi Yang +3

Cultural bias is pervasive in many large language models (LLMs), largely due to the deficiency of data representative of different cultures. Typically, cultural datasets and benchm…

cs.LG2024★ 6 cited

Position: What Can Large Language Models Tell Us about Time Series Analysis

Ming Jin, Yifan Zhang, Wei Chen +6

Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications. Although large language models (LLMs) have recently mad…