Region Comprehension Index (RCI)

• RCI is a model-based metric that quantifies the balance between global and localized visual reasoning in multimodal benchmarks.
• It systematically compares full-image performance with patch-based evaluations to reveal biases in visual reasoning.
• RCI informs dataset curation and model selection by distinguishing tasks that require holistic scene understanding from those exploiting local cues.

The Region Comprehension Index (RCI) is a model-based metric that quantifies the degree to which multimodal benchmarks require global versus localized visual reasoning. RCI was introduced specifically to address the ambiguity in existing evaluations for vision-language tasks, where high benchmark scores may reflect successful exploitation of localized visual cues rather than true holistic scene understanding. RCI enables systematic diagnosis of reasoning bias in large-scale benchmarks, informing both dataset curation and model development toward robust, real-world applications (Agarwal et al., 28 Sep 2025).

1. Formal Definition and Measurement Protocol

RCI is computed by systematically comparing reference-model performance on full images with its performance on constituent image patches. Let $n$ denote the patch grid size (e.g., %%%%1%%%% for a $2\times2$ grid, $n=3$ for a $3\times3$ grid). Each image is partitioned into $n\times n$ non-overlapping patches.

Two key quantities are extracted:

• Full Image Performance (FIP): The reference model’s score on the entire image (using the benchmark’s native metric, such as accuracy or CIDEr).
• Maximum Patch Performance ($\mathrm{MPP}_n$): For each image, the model is evaluated independently on each patch; the highest per-patch score is taken per image and aggregated across the dataset.

The RCI score for granularity $n$ is then formulated as:

$$\mathrm{RCI}_n = 1 - \frac{\mathrm{MPP}_n}{\mathrm{FIP}}$$

If $\mathrm{MPP}_n$ approaches FIP, the benchmark can often be solved by attending to only a localized subset of the image, resulting in low or negative RCI, whereas a higher RCI indicates substantial reliance on global visual reasoning.

RCI leverages the benchmark’s own scoring function and does not require additional annotation, ensuring compatibility and ease of adoption.

2. Patch-Based Evaluation and RCI Computation

The methodology involves systematic spatial partitioning and multiple forward passes:

• Each image in the benchmark is divided into $n \times n$ patches.
• The reference multimodal model is evaluated independently on each patch and on the full image using the same input protocol.
• For each sample, the patch with the highest score is selected ($\mathrm{MPP}_n$).
• The population-level RCI is computed across all test instances.

A validity constraint is imposed: FIP must exceed a minimum accuracy or score threshold unique for each benchmark, so that RCI conveys meaningful reasoning information. Fine-grained grids ($n>3$) are discouraged because excessive partitioning fragments semantic content, artificially inflating RCI and undermining interpretability.

3. Empirical Benchmarks and Observed Biases

RCI was applied to 13 widely used multimodal datasets, including BLINK, GQA, HallusionBench, RealWorldQA, ChartQA, ScienceQA, and TextVQA. The analysis revealed:

• Many datasets (e.g., BLINK, GQA, HallusionBench, RealWorldQA) exhibit negative RCI values, meaning models often perform better using select image patches than with the complete image. This suggests benchmarks predominantly test for localized reasoning and may allow shortcut exploitation.
• Others (e.g., ChartQA, ScienceQA, TextVQA) display positive or near-neutral RCI, indicative of benchmarks where correct task execution depends on holistic understanding.
• Spatial bias was systematically observed: heatmaps show performance is highest for center patches in a $3\times3$ grid, reflecting central placement of critical content and peripheral underrepresentation.

Such findings suggest that benchmarks with low or negative RCI inadvertently encourage development of models that rely on local cues, potentially compromising generalization in real-world situations that require distributed, global comprehension.

4. Applications in Dataset Curation, Model Selection, and System Development

RCI equips both researchers and practitioners with an actionable tool for evaluating and selecting datasets suitable for specific vision-language tasks:

• Tasks demanding holistic spatial context (e.g., autonomous driving, remote sensing) should exhibit high RCI, indicating that successful modeling requires broad visual integration.
• Domains inherently local (e.g., medical imaging, facial recognition) are adequately assessed with lower RCI benchmarks.
• RCI can guide dataset designers to adjust spatial layouts and label assignments so that global reasoning is required for high performance.
• Continuous computation of RCI after data collection or model updates provides diagnostic feedback, ensuring that deployed systems maintain desired reasoning properties and are not vulnerable to spatial shortcuts or adversarial exploitation.

5. Interpretative Scope, Limitations, and Future Directions

RCI is fundamentally model-dependent: while the relative ranking of datasets was shown to be robust across diverse models, absolute values are subject to the architecture selected for reference evaluation. Future work may employ model ensembles or propose standardized reference models to reduce this dependency. Computational cost is notable; RCI calculation requires $n^2 + 1$ evaluations per image, with practical granularity typically limited to $n=2$ or $n=3$.

RCI is currently defined for static, single-image evaluation; extension to video, sequential inference, and multi-image tasks is an open direction. The metric is sensitive to data domain: certain applications naturally depend on local regions. Future RCI adaptations may introduce task-aware normalization or adversarial protocols to further counteract shortcut learning.

6. Relationship to Recent Region-Level Model Innovations

RCI sits within a broader research context of region-level modeling (RegionBLIP (Zhou et al., 2023), VLM-R$^3$ (Jiang et al., 22 May 2025), and RICE (Xie et al., 26 Jul 2025)), which have advanced extraction and reasoning over spatial object regions. While these works focus on enhancing region comprehension and explicit reasoning, RCI allows for quantitative dataset auditing to ensure that such enhanced capabilities are not misaligned by poorly designed benchmarks. A plausible implication is that joint use of RCI and region-aware models may yield improved, bias-corrected evaluation pipelines for large-scale multimodal learning.

7. Summary Table: Key RCI Evaluation Components

Term	Definition	Role in RCI Evaluation
FIP	Model score on full image	Baseline global reasoning metric
MPP$_n$	Highest score among $n^2$ patches per sample	Local reasoning metric
RCI$_n$	$1 - \frac{\mathrm{MPP}_n}{\mathrm{FIP}}$	Quantifies global vs local bias

The RCI offers a systematic, model-based means to measure the reasoning depth required by multimodal benchmarks. By illuminating the global-local continuum of visual information integration, it supports the construction and analysis of datasets and evaluation protocols fundamental to progress in vision-language intelligence (Agarwal et al., 28 Sep 2025).