Nodes Are Early, Edges Are Late: Probing Diagram Representations in Large Vision-Language Models
arXiv:2603.02865v1 Announce Type: new Abstract: Large vision-language models (LVLMs) demonstrate strong performance on diagram understanding benchmarks, yet they still struggle with understanding relationships between elements, particularly those represented by nodes and directed edges (e.g., arrows and lines). To investigate the underlying causes of this limitation, we probe the internal representation of LVLMs using a carefully constructed synthetic diagram dataset based on directed graphs. Our probing experiments reveal that edge information is not linearly separable in the vision encoder and becomes linearly encoded only in the text tokens in the language model. In contrast, node information and global structural features are already linearly encoded in individual hidden states of the vision encoder. These findings suggest that the stage at which linearly separable representations are formed varies depending on the type of visual information. In particular, the delayed emergence o
arXiv:2603.02865v1 Announce Type: new Abstract: Large vision-language models (LVLMs) demonstrate strong performance on diagram understanding benchmarks, yet they still struggle with understanding relationships between elements, particularly those represented by nodes and directed edges (e.g., arrows and lines). To investigate the underlying causes of this limitation, we probe the internal representation of LVLMs using a carefully constructed synthetic diagram dataset based on directed graphs. Our probing experiments reveal that edge information is not linearly separable in the vision encoder and becomes linearly encoded only in the text tokens in the language model. In contrast, node information and global structural features are already linearly encoded in individual hidden states of the vision encoder. These findings suggest that the stage at which linearly separable representations are formed varies depending on the type of visual information. In particular, the delayed emergence of edge representations may help explain why LVLMs struggle with relational understanding, such as interpreting edge directions, which require more abstract, compositionally integrated processes.
Executive Summary
This study investigates the internal representation of large vision-language models (LVLMs) in their ability to comprehend diagrammatic elements, particularly nodes and directed edges. Using a synthetic graph-based dataset, the authors uncover a nuanced disparity: edge information is not linearly separable in the vision encoder but is encoded linearly only in the text tokens of the language model, whereas node information and global structural features are already linearly encoded in the vision encoder. These findings suggest that the timing of linear separability varies by visual element type, with edges emerging later than nodes. This temporal divergence may explain LVLMs’ relative weakness in relational understanding—specifically, interpreting directional relationships—which demand higher-order compositional integration. The study offers a clear, empirically grounded explanation for a persistent limitation in LVLM diagram comprehension.
Key Points
- ▸ Edge representations emerge later than node representations in LVLMs
- ▸ Node information and global structure are linearly encoded in vision encoder states
- ▸ Edge linear separability is delayed until text token processing
Merits
Empirical Rigor
The use of a synthetic, targeted dataset enables precise probing of representation dynamics, enhancing credibility of findings.
Demerits
Limited Scope
Findings are based on synthetic data; generalizability to real-world diagrams or diverse LVLM architectures remains unverified.
Expert Commentary
The paper makes a substantial contribution by isolating a subtle yet critical mechanism in LVLM representation formation. The distinction between node and edge linearity is not merely technical—it has profound implications for how we interpret the cognitive architecture of multimodal models. The delayed emergence of edge representations aligns with broader theories of compositional processing in neural networks, suggesting that LVLMs may inherently prioritize local, node-centric features before integrating global relational structures. This insight could inform future research in multimodal cognition, particularly in areas like causality, temporal reasoning, or schematic inference. Moreover, the paper’s methodological precision—leveraging the synthetic dataset to control for confounding variables—sets a new standard for probing studies in this domain. While the findings are compelling, future work should validate these patterns across heterogeneous LVLM variants and real-world datasets to ensure robustness beyond the experimental constraints.
Recommendations
- ✓ 1. Incorporate targeted edge-encoding augmentation in LVLM pre-training to mitigate relational comprehension gaps.
- ✓ 2. Develop evaluation benchmarks that specifically measure edge-relation inference to better assess progress in LVLM capabilities.