mlsanityData to 14 Oct 2026

MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision

arXiv 2610.17856 ↗First posted 13 Oct 2026cs.LG cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning also fits: Chart understanding and anomaly detection

Abstract (arXiv)

Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations derived from the same signal. This leaves open whether multimodal large language models (MLLMs) can integrate time series with real-world visual evidence. We introduce MMTA (multimodal temporal analysis), a benchmark of tri-modal tasks with diverse cross-modal complementarity patterns among time series, text, and vision. MMTA spans nine domains -- robotics, autonomous driving, human action, manufacturing, AR assistance, healthcare, recommendation, finance, and climate -- under video+TS and image+TS layouts, with a 3,524-sample test split covering classification and forecasting. The benchmark standardizes heterogeneous real-world sources into a shared schema with domain-specific prompts, provenance documentation, and success-weighted metrics that penalize both wrong answers and failed generations. Evaluating eight open-source and proprietary MLLMs reveals a shared bottleneck: representing time series as text creates very long token sequences and weakens fine-grained alignment with visual evidence, especially in long-video settings. We then introduce TimeOmni-v, a multimodal LLM extension with a dedicated time-series encoder, temporal positional alignment, modality-aware token layout, and a forecasting head. TimeOmni-v improves long-video classification and time-series inference efficiency, showing that MMTA not only exposes representation and grounding failures but also motivates targeted model design.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 32 of its 37 references that are in the map. First posted on arXiv 13 Oct 2026.

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32 of 37 references.

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Same direction, newest first

  1. 2025Qwen3-VL Technical Report26 Nov

Other references · 31

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2025InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiencycited by 1,510
  4. 2025Qwen2.5-Omni Technical Reportcited by 736
  5. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  6. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 597
  7. 2025Qwen3-Omni Technical Reportcited by 497
  8. 2025Chronos-2: From Univariate to Universal Forecastingcited by 194
  9. 2023HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real Worldcited by 166
  10. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoningcited by 103
  11. 2024From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflectioncited by 98
  12. 2024Context is Key: A Benchmark for Forecasting with Essential Textual Informationcited by 87
19 more
  1. 2025Time-MQA: Time Series Multi-Task Question Answering with Context Enhancementcited by 82
  2. 2024CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Drivingcited by 77
  3. 2024Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysiscited by 74
  4. 2025MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answeringcited by 55
  5. 2025TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agentscited by 37
  6. 2025ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Datasetcited by 37
  7. 2024A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualizationcited by 33
  8. 2025Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Seriescited by 19
  9. 2023An Image Dataset for Benchmarking Recommender Systems with Raw Pixelscited by 18
  10. 2025SciTS: Scientific Time Series Understanding and Generation with LLMscited by 15
  11. 2025FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysiscited by 11
  12. 2026MMTS-BENCH: A Comprehensive Benchmark for Time Series Understanding and Reasoningcited by 9
  13. 2024Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platformcited by 8
  14. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 8
  15. 2025Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Datacited by 7
  16. 2025MoTime: A Dataset Suite for Multimodal Time Series Forecastingcited by 7
  17. 2026Rethinking Multimodal Time-Series Forecasting Evaluationcited by 7
  18. 2026Empowering Time Series Analysis with Large-Scale Multimodal Pretrainingcited by 3
  19. 2025A Large-Scale Multimodal Dataset and Benchmarks for Human Activity Scene Understanding and Reasoningcited by 2

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