HighlightBench: Benchmarking and Diagnosing Markup-Driven Table Reasoning in Scientific Documents

Visual markups such as highlights, underlines, and bold textare common in table-centric documents. Although multimodal large lan-guage models (MLLMs) have made substantial progress in document un-derstanding, their ability to treat such cues as explicit logical directivesremains under-explored. More importantly, existing evaluations cannotdistinguish whether a model fails to see the markup or fails to reason withit. This creates a key blind spot in assessing markup-conditioned behaviorover tables. To address this gap, we introduce HighlightBench, a diagnos-tic benchmark for markup-driven table understanding that decomposesevaluation into five task families: Markup Grounding, Constrained Re-trieval, Local Relations, Aggregation & Comparison, and Consistency &Missingness. We further provide a reference pipeline that makes interme-diate decisions explicit, enabling reproducible baselines and finer-grainedattribution of errors along the perception-to-execution chain. Experi-ments show that even strong models remain unstable when visual cuesmust be consistently aligned with symbolic reasoning under structuredoutput constraints.