SVE: Structured Visual Enhancement

• Structured Visual Enhancement (SVE) is a framework that converts raw visual inputs into explicit, machine-readable representations, decoupling visual perception from reasoning.
• It applies structured encoding methods—such as tokenization, graph-based parsing, and vectorization—across domains like chemistry, math, navigation, and scene understanding.
• Empirical results demonstrate significant performance gains in accuracy and problem-solving by identifying and isolating errors inherent to visual parsing.

Structured Visual Enhancement (SVE) is a paradigm and toolkit for enhancing multimodal AI models by converting raw visual inputs into explicit, machine-readable structured representations. SVE methods systematically disentangle the difficulties of visual perception from domain-specific reasoning by encoding graphical, geometric, or relational elements—such as chemical diagrams, mathematical primitives, spatial layouts, or scene graphs—into symbolic tokens, vector databases, or annotated graphs. This approach has achieved demonstrable gains in fields including automated chemistry problem-solving, mathematical reasoning, visual navigation, floor plan interpretation, and general scene understanding, as documented in recent literature (Qiang et al., 20 Nov 2025, Lee et al., 5 Nov 2025, Zhang et al., 11 Jan 2025, Kuo et al., 2022, Herzig et al., 2023).

1. Formal Definitions and Core Objectives

SVE is operationally defined as a mapping from a visual domain (images, diagrams, video frames) to a structured symbolic or vector domain that captures explicit primitives and semantics. In ChemO (Qiang et al., 20 Nov 2025), SVE augments each assessment-equivalent reformulation (AER) problem $\mathcal{P}_{\mathrm{AER}}$ with a tuple $\mathcal{G}$ containing machine-verifiable objects: SMILES strings, mechanism graphs, or spectra encodings. More generally, SVE maps an image $V$ to $\mathcal{G} = \{s_i\}$, where each $s_i$ is a structured description of a visual constituent (molecule, geometric shape, room polygon, object-attribute-relation triple):

$$f_{\mathrm{SVE}}: V \rightarrow \{s_i\in \mathcal{S}\}$$

Objectives across domains include: (a) bypassing model-specific visual parsing bottlenecks, (b) enabling precise diagnosis of reasoning versus perception errors, and (c) facilitating plug-and-play integration with downstream multimodal agents or LLMs.

2. Methodological Variants and Architectural Taxonomy

SVE encompasses a range of instantiations, varying with application context:

• Chemistry: Tool-based parsers (e.g., OCSR, expert curation) generate symbolic representations (SMILES, InChI) for each compound or reaction diagram. These are fed into agent-based solvers and evaluated via graph isomorphism and quantitative match scores (Qiang et al., 20 Nov 2025).
• Mathematics (SVE-Math): Auxiliary geometric encoders (GeoGLIP) extract bounding boxes, junctions, and boundaries from diagrams and route only the most salient features into the main LLM using dynamic, problem-adaptive weighting mechanisms (Zhang et al., 11 Jan 2025).
• Spatial Layout (SVG SVE): Floor plans and network diagrams are raster–vector decomposed into SVG primitives (polygons, text labels, lines, groups), improving count and labeling accuracy but posing trade-offs for connectivity/pathfinding tasks (Lee et al., 5 Nov 2025).
• Navigation (SEA SVE): Structure-Encoding Auxiliary tasks train encoders to predict local geometric and semantic structure (3D jigsaw, traversability, instance classification), yielding plug-in feature banks for VLN agents (Kuo et al., 2022).
• Scene Graphs (SGVL SVE): Transformer-based vision models are augmented with object and relation tokens, supervised to predict scene graph nodes and edges, and aligned with graph-conditional captions for rich multimodal fusion (Herzig et al., 2023).

A common thread is the decoupling of representation learning (explicit structure extraction) from the primary reasoning engine.

3. Integration Strategies and Evaluation Frameworks

SVE can be integrated in several pipeline locations and modalities:

• As a plug-in to perception modules, replacing traditional end-to-end vision encoders with structured symbol feeds (ChemLabs Perception Lab, SEA feature banks, SVG+PNG prompt concatenation).
• Through dynamic feature routers, controlling the granularity and relevance of injected features on a per-instance basis to avoid information overload (SVE-Math router (Zhang et al., 11 Jan 2025)).
• For language-model fusion, using channel or sequence concatenation of visual and symbolic embeddings, followed by downstream reasoning and output generation.
• All variants preserve original grading or loss functions, so that performance shifts directly reflect perception-versus-reasoning improvement rather than changes in scoring criteria.

Evaluation is conducted by contrasting baseline performance (vision-only, raw images) to SVE-augmented pipelines, measuring:

• Graph isomorphism and similarity for chemical structure construction.
• Exact-match accuracy and F1 for component counting and labeling (SVG SVE).
• Pathfinding validity/perfection under induced adjacency graphs (SVG SVE).
• Navigation success rate (SR), success weighted by path length (SPL) in VLN agents (SEA).
• Compositional retrieval and attribute/relation modification scores (SGVL).

4. Empirical Impact and Cross-Domain Performance

SVE demonstrates substantial empirical gains, often surpassing the improvements achievable by scaling model size or data:

Domain	SVE Variant	Score Gain	Baseline	SVE	Reference
Chemistry	SVE + MAS	+23.0	70.6	93.6	(Qiang et al., 20 Nov 2025)
Math	SVE-Math	+15.0	21.2	31.4	(Zhang et al., 11 Jan 2025)
Navigation	SEA SVE	+12.0	0.35	0.47	(Kuo et al., 2022)
Spatial	SVG+PNG	+0.21	0.75	0.96	(Lee et al., 5 Nov 2025)
Scene Graph	SGVL	+1.8–6.6	—	—	(Herzig et al., 2023)

These results confirm that imperfect visual parsing, particularly in multimodal tasks requiring fine-grained spatial or relational understanding, is a primary bottleneck. SVE unlocks fast gains by making visual semantics explicit for the model.

5. Limitations and Contextual Trade-offs

Several limitations are acknowledged:

• Dependency on external tools and manual annotation: Most SVE pipelines rely on either off-the-shelf parsers (e.g., OCSR, SVG extractors) or expert validation. Structured encodings can introduce bias or fail to capture nuanced elements (e.g., stereochemistry, subtle spatial gradients).
• Fragmentation in holistic tasks: In spatial reasoning tasks, decomposition into primitives sometimes disrupts models’ global connectivity inference or introduces hallucinations in token sequences (SVG SVE, Llama’s phantom rooms).
• Not a substitute for robust multimodal representation learning: SVE is diagnostic and not designed for deployment in unconstrained, open-world settings.
• Generalization and scalability: Structured extraction often covers only a subset of possible input domains; extending to spectra, 3D configurations, abstract diagrams, or real-world navigation remains open.

6. Future Directions and Extensions

Proposed developments include:

• Automated and adaptive vision modules: Tight coupling of SVE extraction with end-to-end trainable vision encoders, reducing manual annotation burden.
• Broader domain transfer: Extending SVE methodologies to statistical charts, network diagrams, and more abstract representations.
• Adversarial and dynamic prompting: Designing adversarial examples to benchmark SVE’s robustness, and exploring interactive, dynamic prompt generation to support reasoning.
• Weakly-supervised pretraining and interpretable routing: Leveraging SVE as an auxiliary supervision signal to improve multimodal pretraining efficiency, and introducing interpretable feature selection interfaces.
• Integration with semantic annotations and natural-language region descriptors to supplement vector decompositions and scene graphs.

7. Interpretive Significance and Research Landscape

SVE marks a transition toward explicit separation of perception and reasoning in multimodal systems. The framework unifies disparate advances across chemistry, mathematical problem solving, spatial layout comprehension, navigation, and scene composition. Its principled formalization, implementation flexibility, and consistent performance gains underscore its value as both a diagnostic tool and a practical enhancement for next-generation AI systems addressing complex visual reasoning tasks. Structured Visual Enhancement will likely inform future directions in transparent, interpretable design of multimodal neural architectures.