DocHop: Benchmarking Out-of-domain Multi-hop Reasoning in Information-Dense Documents
Multimodal Large Language Models (MLLMs) have achieved strong performance on structured visual understanding tasks such as chart and document question answering. However, existing benchmarks typically evaluate these domains in isolation, leaving underexplored a key capability: whether models can use textual context to determine how chart evidence should be selected, interpreted, and aggregated. We introduce DocHop, a benchmark for integrated chart--context reasoning in document-style images. In DocHop, the document narrative specifies multi-step compositional constraints, while charts provide the corresponding data values. Questions are grounded on a semantic reference label defined in th...
| Authors | Zhuoran Yu, Le Thien Phuc Nguyen, Jaden Park et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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