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From the 2 of 20 linked papers with an AI index.

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20242026
most citedTimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop

2 citations · 3 across the 4 of their papers we have counts for

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cs.LG20262 cited

TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop

Yushan Jiang, Wenchao Yu, Geon Lee +5

Time series analysis provides essential insights for real-world system dynamics and informs downstream decision-making, yet most existing methods often overlook the rich contextual…

cs.LG2025

On the Effect of Sampling Diversity in Scaling LLM Inference

Tianchun Wang, Zichuan Liu, Yuanzhou Chen +5

Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it. Motivated by the observed…

cs.LG2025

DISC: Dynamic Decomposition Improves LLM Inference Scaling

Jonathan Light, Wei Cheng, Benjamin Riviere +6

Inference scaling methods for LLMs often rely on decomposing problems into steps (or groups of tokens), followed by sampling and selecting the best next steps. However, these steps…

cs.LG2025

Harnessing Vision Models for Time Series Analysis: A Survey

Jingchao Ni, Ziming Zhao, ChengAo Shen +5

Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs).…

cs.LG2025

Where's the liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content

Haoyue Bai, Yiyou Sun, Wei Cheng +1

The recent proliferation of photorealistic images created by generative models has sparked both excitement and concern, as these images are increasingly indistinguishable from real…

cs.LG2024

PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization

Yuyang Ye, Lu-An Tang, Haoyu Wang +5

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity fo…