mlsanityData to 14 Oct 2026

From Consistency to Complementarity: Aligned and Disentangled Multi-modal Learning for Time Series Understanding and Reasoning

arXiv 2601.21436 ↗First posted 29 Jan 2026cs.LG cs.AI cs.CLcited by 0

On the map: LLM reasoning → Time series reasoning

also in 1 more
Abstract (arXiv)

Advances in multi-modal large language models (MLLMs) have inspired time series understanding and reasoning tasks, that enable natural language querying over time series, producing textual analyses of complex temporal dynamics. Recent attempts hybridize numerical time series with their visualized plots, facilitating precise value reasoning and visual structure comprehension for comprehensive time series understanding of MLLMs. However, effective numerical-visual modality integration remains challenging due to fine-grained temporal misalignment across modalities and severe entanglement between shared and modality-specific semantics, which hinder localized interpretation and complementary reasoning. To address these issues, we propose MADI, a multi-modal LLM enhanced with fine-grained alignment and disentangled interaction, featuring (1) Patch-level Alignment, which enforces physically grounded fine-grained correspondence across heterogeneous modalities, (2) Discrete Disentangled Interaction, which separates modality-common semantics into compact discrete latents and adaptively synergizes the purified modality-unique information, and (3) Critical-token Highlighting, which emphasizes informative, query-relevant signals for robust reasoning. Experiments on synthetic and real-world benchmarks show that MADI consistently outperforms general-purpose LLMs and time-series-specialized MLLMs.

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 44 references that are in the map. First posted on arXiv 29 Jan 2026.

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14 of 44 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning16 Jun
  2. 2025MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering21 Mar

Other references · 12

  1. 2025Qwen3 Technical Reportcited by 8,820
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2025Qwen2.5-VL Technical Reportcited by 5,587
  4. 2024Qwen2.5 Technical Reportcited by 4,942
  5. 2024GPT-4o System Cardcited by 4,588
  6. 2025Qwen3-VL Technical Reportcited by 2,793
  7. 2025DeepSeek-V3.2: Pushing the Frontier of Open Large Language Modelscited by 815
  8. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 197
  9. 2025DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learningcited by 24
  10. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?cited by 12
  11. 2025From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?cited by 7
  12. 2025Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectivescited by 7

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  4. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
  5. 2024TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment
  6. 2025Multimodal Language Models with Modality-Specific Experts for Financial Forecasting from Interleaved Sequences of Text and Time Series
  7. 2024Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
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