mlsanityData to 14 Oct 2026

TS-HINT: Enhancing Semiconductor Time Series Regression Using Attention Hints From Large Language Model Reasoning

arXiv 2512.05419 ↗First posted 5 Dec 2025cs.LGcited by 0

On the map: LLM reasoning → Time series reasoning

Abstract (arXiv)

Existing data-driven methods rely on the extraction of static features from time series to approximate the material removal rate (MRR) of semiconductor manufacturing processes such as chemical mechanical polishing (CMP). However, this leads to a loss of temporal dynamics. Moreover, these methods require a large amount of data for effective training. In this paper, we propose TS-Hint, a Time Series Foundation Model (TSFM) framework, integrated with chain-of-thought reasoning which provides attention hints during training based on attention mechanism data and saliency data. Experimental results demonstrate the effectiveness of our model in limited data settings via few-shot learning and can learn directly from multivariate time series features.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 14 of its 22 references that are in the map. First posted on arXiv 5 Dec 2025.

Builds on

14 of 22 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model20 May
  2. 2024Towards Time Series Reasoning with LLMs17 Sep

Other references · 12

  1. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  3. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  4. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  5. 2023A decoder-only foundation model for time-series forecastingcited by 692
  6. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  7. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 614
  8. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 482
  9. 2023TimeGPT-1cited by 158
  10. 2019Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Groundedcited by 128
  11. 2024LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modelingcited by 3
  12. 2025Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMscited by 1

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  1. 2026LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing
  2. 2026TS-MLLM: A Multi-Modal Large Language Model-based Framework for Industrial Time-Series Big Data Analysis
  3. 2026SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
  4. 2025TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction
  5. 2026TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models
  6. 2025TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning
  7. 2026From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs
  8. 2026VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence