mlsanityData to 14 Oct 2026

From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs

arXiv 2606.22126 ↗First posted 20 Jun 2026cs.CLcited by 0

On the map: LLM reasoning → Time series reasoning

Abstract (arXiv)

Time series analysis has recently been coupled with Large Language Models (LLMs) to leverage their reasoning and world knowledge capabilities, yet gains remain limited. We attribute this to a fundamental mismatch between existing task formulations and LLM strengths: most settings reduce time series understanding to curve-fitting systems, focusing on low-level prediction while ignoring the semantic, contextual, and reasoning-intensive nature of real-world temporal decision-making.To address these limitations, we introduce TSCognition, a multimodal benchmark for multi-dimensional time series reasoning. It collects real-world time series and textual information from 15 public sources and constructs approximately 41K QA samples around five cognitive reasoning tasks: Decoding, Grounding, Inferring, Extrapolating, and Acting. Building on this, we further propose TSAlign, a unified framework that encodes time series into compact patch-level representations and aligns them with semantic directions in the LLM embedding space via gated residual injection and multivariate fusion.Experiments show that TSAlign outperforms existing LLM, VLM, and time series QA baselines on TSCognition and the publicly available TimerBed benchmark while substantially reducing computational cost.Code is available at: [https://github.com/EIT-NLP/CognitiveTSR](https://github.com/EIT-NLP/CognitiveTSR)

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 18 of its 52 references that are in the map. First posted on arXiv 20 Jun 2026.

Builds on

18 of 52 references.

Closest ancestors · 6

Same direction, newest first

  1. 2026Rethinking the Role of LLMs in Time Series Forecasting16 Feb
  2. 2026TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models26 Jan
  3. 2026TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation20 Jan
  4. 2025SciTS: Scientific Time Series Understanding and Generation with LLMs26 Sep
  5. 2025Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs12 Jun
  6. 2025MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering21 Mar

Other references · 12

  1. 2024Qwen2.5 Technical Reportcited by 4,942
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  3. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  4. 2026It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarkscited by 33
  5. 2025TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Modelscited by 27
  6. 2026TSAQA: Time Series Analysis Question And Answering Benchmarkcited by 11
  7. 2026ViCA: Efficient Multimodal LLMs with Vision-Only Cross-Attentioncited by 10
  8. 2024On the Feasibility of Vision-Language Models for Time-Series Classificationcited by 8
  9. 2026TimeOmni-VL: Unified Models for Time Series Understanding and Generationcited by 7
  10. 2025The Few Govern the Many:Unveiling Few-Layer Dominance for Time Series Modelscited by 3
  11. 2026Prompting Underestimates LLM Capability for Time Series Classificationcited by 1
  12. 2026Patch-Level Tokenization with CNN Encoders and Attention for Improved Transformer Time-Series Forecastingcited by 1

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  1. 2026Thoth: Mid-Training Bridges LLMs to Time Series Understanding
  2. 2025TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction
  3. 2025Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
  4. 2026LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics
  5. 2025Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data
  6. 2025TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning
  7. 2024A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization
  8. 2024Towards Time Series Reasoning with LLMs