RealCQA-V2: A Diagnostic Benchmark for Structured Visual Entailment over Scientific Charts

TL;DR

RealCQA-V2 is a new benchmark for structured visual entailment in scientific charts, enabling atomic reasoning verification and diagnosing multimodal reasoning capabilities of models.

Contribution

It introduces a structured logical entailment task over chart elements, with chain-level metrics, to evaluate and diagnose multimodal reasoning in scientific chart understanding.

Findings

Models verify individual premises well but struggle with full chain coherence.

RealCQA-V2 provides a reproducible benchmark for structured visual entailment.

Baseline LVLMs show a reasoning gap between local premise verification and global chain coherence.

Abstract

Multimodal reasoning models often produce fluent answers supported by seemingly coherent rationales. Existing benchmarks evaluate only final-answer correctness. They do not support atomic visual entailment verification of intermediate steps, especially visual compositional logic. This limitation is especially acute in scientific chart understanding, where answers depend on deterministically grounded visual semantics such as axes, legends, and quantitative relations. We introduce RealCQA-V2, a large-scale benchmark that reformulates chart question answering as Visual Premise Proving (VPP): a structured logical entailment task over chart-grounded visual predicates. Each question is deconstructed into manually curated, atomic premises grounded in chart elements (axes, legends, marks, and quantitative relations), yielding executable reasoning chains rather than free-form textual rationales. These premises form compositional reasoning chains, enabling verification at the level of individual visual statements and complete reasoning sequences. We introduce chain-level metrics that measure both full logical validity (AccVPP) and partial reasoning progress within failed chains (DCP), extending beyond traditional VQA accuracy. Baseline evaluations across representative LVLMs reveal a consistent local-global reasoning gap: models often verify many individual premises correctly while failing to preserve coherence across the full chain. RealCQA-V2 establishes a reproducible benchmark for structured visual entailment over real scientific charts and enables rigorous diagnosis of multimodal reasoning beyond answer-only evaluation.