# Chart understanding and anomaly detection

Area: [Multimodal LLMs / vision-language models](https://mlsanity.com/mllm/index.md). HTML: https://mlsanity.com/mllm/chart-understanding-and-anomaly-detection/

## Key facts

- Chart understanding and anomaly detection: 396 papers on arXiv since 2022 (data through 14 Oct 2026).
- 32 of its 396 mapped papers (8%) cite “ChartLlama: A Multimodal LLM for Chart Understanding and Generation” (2023, arXiv 2311.16483).
- 30 of its 396 mapped papers (8%) cite “CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs” (2024, arXiv 2406.18521).
- 14 new papers 17 Sep–14 Oct.

## Most cited here

- [ChartLlama: A Multimodal LLM for Chart Understanding and Generation](https://mlsanity.com/paper/2311.16483/) (2023), cited by 32 papers here
- [CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://mlsanity.com/paper/2406.18521/) (2024), cited by 30 papers here
- [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://mlsanity.com/paper/2212.09662/) (2022), cited by 28 papers here
- [ChartBench: A Benchmark for Complex Visual Reasoning in Charts](https://mlsanity.com/paper/2312.15915/) (2023), cited by 27 papers here
- [UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning](https://mlsanity.com/paper/2305.14761/) (2023), cited by 25 papers here
- [MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning](https://mlsanity.com/paper/2311.10774/) (2023), cited by 23 papers here
- [ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning](https://mlsanity.com/paper/2402.12185/) (2024), cited by 23 papers here
- [ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning](https://mlsanity.com/paper/2401.02384/) (2024), cited by 19 papers here
- [AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models](https://mlsanity.com/paper/2308.15366/) (2023), cited by 17 papers here
- [VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2308.11681/) (2023), cited by 12 papers here

## New (17 Sep–14 Oct)

- [Is In-Domain Training Enough for Fine-Grained Industrial Anomaly Understanding?](https://mlsanity.com/paper/2610.12310/) (2026-10-08)
- [DataVista: Diagnosing Multimodal LLMs on Data Video Understanding](https://mlsanity.com/paper/2610.11993/) (2026-10-08)
- [TaoD2C-Bench: Benchmarking MLLMs for Industrial UI Code Generation Beyond Visual Fidelity](https://mlsanity.com/paper/2610.10374/) (2026-10-07)
- [ChartBmkAgent: Harness-Governed Multi-Agent Construction of Chart QA Benchmarks from Sparse Error-Taxonomy Specifications](https://mlsanity.com/paper/2610.08106/) (2026-10-06)
- [CVIF: A Criticality-Driven Visual Intervention Framework for Geometric Diagram Understanding in MLLMs](https://mlsanity.com/paper/2610.06399/) (2026-10-05)
- [Knossos and Ariadne: Benchmarking and Learning Complete Diagram Topology Extraction with Vision-Language Models](https://mlsanity.com/paper/2610.04721/) (2026-10-03)
- [OSWorld-Science: A Benchmark of Computer Use Agents for Learning and Using Scientific Software](https://mlsanity.com/paper/2609.39903/) (2026-09-30)
- [TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization](https://mlsanity.com/paper/2609.39033/) (2026-09-30)
- [ChartDensity-Bench: Benchmarking MLLMs for Numerical Data Reconstruction under Visual Density](https://mlsanity.com/paper/2609.38781/) (2026-09-30)
- [K-OPSD: Verifiable On-Policy Self-Distillation for Post-Training Vision-Language Models on AEC Drawings](https://mlsanity.com/paper/2609.34082/) (2026-09-28)
- [Beyond Balanced Accuracy: A Resolution and Parity-Controlled Benchmark for Vision-Language and Vision-Only Defect Assessment in UAV Power-Line Inspection](https://mlsanity.com/paper/2609.27457/) (2026-09-23)
- [Spatial Action Review: A Visual Analytics Dashboard for Auditing Language-to-Action Hand-offs in Electron Microscopy](https://mlsanity.com/paper/2609.24470/) (2026-09-21)
- [PolyBridgeBench: Benchmarking Multimodal LLMs for Physics-Grounded Bridge Design](https://mlsanity.com/paper/2609.21493/) (2026-09-18)
- [Adapting Vision-Language Models for Human-Readable XAI in Industrial Object Detection](https://mlsanity.com/paper/2609.31690/) (2026-09-17)