mlsanityData to 14 Oct 2026

LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics

arXiv 2604.17295 ↗First posted 19 Apr 2026cs.AIcited by 3

On the map: LLM reasoning → Time series reasoning

Abstract (arXiv)

Comprehensive understanding of time series remains a significant challenge for Large Language Models (LLMs). Current research is hindered by fragmented task definitions and benchmarks with inherent ambiguities, precluding rigorous evaluation and the development of unified Time Series Reasoning Models(TSRMs). To bridge this gap, we formalize Time Series Reasoning (TSR) via a four-level taxonomy of increasing cognitive complexity. We introduce HiTSR, a hierarchical time series reasoning dataset comprising 83k samples with diverse task combinations and verified Chain-of-Thought (CoT) trajectories. Leveraging HiTSR, we propose LLaTiSA, a strong TSRM that integrates visualized patterns with precision-calibrated numerical tables to enhance the temporal perception of Vision-Language Models (VLMs). Through a multi-stage curriculum fine-tuning strategy, LLaTiSA achieves superior performance and exhibits robust out-of-distribution generalization across diverse TSR tasks and real-world scenarios. Our code is available at https://github.com/RainingNovember/LLaTiSA.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 26 of its 44 references that are in the map. First posted on arXiv 19 Apr 2026.

Builds on

26 of 44 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025TimeSense:Making Large Language Models Proficient in Time-Series Analysis9 Nov
  2. 2025OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data2 Oct
  3. 2025SciTS: Scientific Time Series Understanding and Generation with LLMs26 Sep
  4. 2025CaTS-Bench: Can Language Models Describe Time Series?25 Sep
  5. 2025When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference1 Sep
  6. 2025Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs12 Jun
  7. 2025MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering21 Mar
  8. 2025Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data13 Mar

Other references · 18

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2025Qwen3-VL Technical Reportcited by 2,793
  4. 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,834
  5. 2025Gemma 3 Technical Reportcited by 1,824
  6. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  7. 2021Monash Time Series Forecasting Archivecited by 267
  8. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 208
  9. 2025DeepSeek-OCR: Contexts Optical Compressioncited by 183
  10. 2024Context is Key: A Benchmark for Forecasting with Essential Textual Informationcited by 87
  11. 2024TimeSeriesExam: A time series understanding examcited by 49
  12. 2024Teach Multimodal LLMs to Comprehend Electrocardiographic Imagescited by 28
6 more
  1. 2025TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Modelscited by 27
  2. 2025BEDTime: A Unified Benchmark for Automatically Describing Time Seriescited by 12
  3. 2025FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysiscited by 11
  4. 2026MMTS-BENCH: A Comprehensive Benchmark for Time Series Understanding and Reasoningcited by 9
  5. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 8
  6. 2025HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understandingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Representing Time Series as Structured Programs for LLM Reasoningcited by 2
  2. 2026TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Modelscited by 0
  3. 2026OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs
  2. 2024A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization
  3. 2026Thoth: Mid-Training Bridges LLMs to Time Series Understanding
  4. 2024Towards Time Series Reasoning with LLMs
  5. 2026TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
  6. 2025Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
  7. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
  8. 2025TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning