VLM-as-Formalizer Pipelines

• VLM-as-Formalizer Pipelines are an architectural paradigm that converts images and text into structured formal outputs for reasoning and planning.
• They integrate multiple legacy components like OCR and object detection into a unified system, reducing error propagation and engineering overhead.
• These pipelines show strong performance in retail VQA and multimodal planning, though challenges remain in fine-grained visual grounding and token efficiency.

Vision-LLMs as Formalizers (“VLM-as-Formalizer Pipelines”) refer to the architectural paradigm in which a vision-LLM, or a hybrid system involving vision and language modalities, takes multimodal input (commonly images and text) and outputs structured formal representations suitable for downstream computation, reasoning, or verification. In production and research contexts, such pipelines are increasingly deployed to bypass legacy multi-stage systems—such as separate OCR, object detection, and classification workflows—by consolidating visual understanding and formalization within a single, end-to-end or modular VLM-based mechanism.

1. Architectural Principles and Pipeline Variants

VLM-as-Formalizer pipelines are constructed to directly translate raw visual inputs (with optional textual context) into structured and formally interpretable outputs. In contrast to classical multitiered systems (e.g., image → OCR → NLP → database), the VLM formalizer functions either as a single-step processor or as the extractor module within a modular pipeline.

The paradigms cataloged in recent work (Lamm et al., 28 Aug 2024, Ye et al., 21 Dec 2024, He et al., 25 Sep 2025) can be organized as follows:

Pipeline Type	Input Modality	Output Formalism
Direct Formalizer	Image (+Text)	Structured Text/Label
Caption/Scene	Image → Caption	Scene Graph → Formal Spec
Hybrid Modular	Image → Text → Logic	Code, PDDL, GraphModel

For example, in retail VQA (Lamm et al., 28 Aug 2024), VLMs are prompted to answer feature-specific queries (e.g., “brand,” “price,” “discount”) directly from image input, whereas for multimodal planning (He et al., 25 Sep 2025), several variants first extract intermediate captions or scene graphs before formalizing the environment in Planning Domain Definition Language (PDDL).

2. Comparative Analysis of VLM Formalization vs. Traditional Pipelines

VLM-as-Formalizer pipelines present the following trade-offs:

• Simplicity and Engineering Overhead: VLM-based solutions remove a suite of legacy components, such as multi-pass OCR, by handling visual cues and textual extraction in one model call (Lamm et al., 28 Aug 2024).
• Unified Processing: These models inherently resolve co-dependent visual-text features, bypassing error propagation associated with sequential OCR/classification pipelines.
• Performance Variance: Although salient features (large font “brand” and “price”) are extracted with high accuracy across both open-source and commercial VLMs, fine-grained extraction (e.g., “discount,” small font attributes) shows substantial deficit even for state-of-the-art systems (Lamm et al., 28 Aug 2024).
• Efficiency Concerns: The single-step VLM may be slower when scaled; for instance, commercial VLM query times reach 2.5 hours for hundreds of queries (Lamm et al., 28 Aug 2024).

Notably, in planning tasks (He et al., 25 Sep 2025), VLMs as formalizers far outperform direct end-to-end planning solutions by translating complex multimodal inputs into explicit PDDL problem files, which can be fed into verifiable solvers. However, poor recall of object relations (limited visual grounding) remains a bottleneck irrespective of improvements in textual reasoning modules.

3. Performance Evaluation and Task-Specific Outcomes

Rigorous evaluations span retail VQA (Lamm et al., 28 Aug 2024), flowchart understanding (Ye et al., 21 Dec 2024), and multimodal planning (He et al., 25 Sep 2025):

• Retail VQA: Acceptable prediction ratios for prominent product features nearly match those produced by robust OCR+LLM pipelines (e.g., GPT-4V only missed one brand prediction in 50 samples; LLM+OCR delivered 42/50 correct brands). Subtle features such as “discount” consistently yield lower precision.
• Flowchart Understanding: TextFlow (Ye et al., 21 Dec 2024) leverages intermediate representations (Graphviz, Mermaid, PlantUML) for decoupled reasoning, enabling state-of-the-art performance on FlowVQA (accuracy increases from 76.61% to 82.74% for Claude-3.5-Sonnet with modular reasoning).
• Multimodal Planning: Scene Graph and Caption-based formalization pipelines deliver higher planning and simulation success rates; however, scene-level metrics (precision, recall, F1 for initial state relations) indicate that exhaustive object-relation extraction from images is lacking (He et al., 25 Sep 2025).

4. Methodological Innovations and Intermediate Representations

Intermediate representation strategies (e.g., captions, scene graphs) are pivotal for improving formalization recall and robustness (Ye et al., 21 Dec 2024, He et al., 25 Sep 2025):

• Textual Intermediate (Caption-P): A detailed caption extracts object types, properties, spatial relationships, and goal-related details, then serves as context for structured formalization to PDDL (He et al., 25 Sep 2025).
• Graph-Based (SG-P, AP-SG-P, EP-SG-P): Scene graphs enumerate grounded instances and binary relations, which are iteratively verified and compiled into formal logic or planning files. The multi-stage affirmation (automatic or explicit predicate verification) improves recall for relational facts but does not fully resolve vision bottlenecks.
• Formal Interface: VLM-extracted intermediate representations not only improve controllability and explainability but enable integration with external reasoning tools or callable APIs (e.g., for graph queries or plan validation).

5. Challenges, Bottlenecks, and Prospective Solutions

Key bottlenecks include:

• Visual Grounding Limitation: The main performance bottleneck is in the vision component—specifically, the exhaustive detection and encoding of object relations and fine-grained attributes (He et al., 25 Sep 2025). This limitation is visible across retail and planning domains.
• Token Efficiency vs. Task Quality: Pipelines using rich captions perform better but with increased token usage, raising concerns for inference scaling.
• Domain-Specific Knowledge: Lack of domain adaptation restricts VLMs from robustly handling abstract or visually subtle queries (e.g., retail “discounts”). Retrieval-Augmented Generation (RAG) methods are posited to enhance performance through contextual reinforcement (Lamm et al., 28 Aug 2024).
• Quality of Intermediate Representations: Although intermediary textual representations compensate for some visual model deficiencies, their gains are inconsistent and the optimal abstraction level remains an open question.

The proposed research directions focus on:

• Enhancing visual perception modules to address object-relation recall.
• Developing richer, hierarchical intermediate representations.
• Improving token efficiency through concise representation and reasoning strategies.
• Integrating external knowledge via RAG or hybrid approaches.

6. Formalization Functions and Technical Representation

Formalization in these pipelines is formally modeled as a mapping:

$A = f(I, Q)$

Where $A$ is the structured answer, $I$ is the image, and $Q$ is the query context. In planning formalization:

• Objects: $E = \{e_1, ..., e_n\}$
• Initial State: $s_0 \in \mathcal{S}$, a fact set $r(\bar{e})$
• Goal State: $$\psi_{\text{goal}} : \mathcal{S} \rightarrow \mathbb{B}$$
• Action Plan: $L = [a_1(\bar{e}_1), ..., a_m(\bar{e}_m)]$

Such representations translate multimodal input into structured formal language interpretable by deterministic solvers or reasoning engines, bridging the gap between end-user tasks and formal computation (He et al., 25 Sep 2025, Ye et al., 21 Dec 2024).

7. Impact and Prospective Applications

VLM-as-Formalizer pipelines substantially influence production and research in visual understanding, automated reasoning, and planning tasks:

• Production-Grade VQA: Broadly reduce engineering overhead for salient feature extraction in retail, while requiring hybridization or fine-tuning for high-precision extraction of subtle features.
• Flowchart and Diagram Understanding: Modular autoformalization frameworks support explainable, robust extraction and reasoning in scientific document understanding and educational tools.
• Multimodal Planning: Enable embodied agents and simulators to benefit from verifiable, deterministic planning, provided that visual object-relation grounding advances.
• Research Directions: Further exploration in integrating external retrieval, fine-grained vision models, and scalable, token-efficient intermediate representations is paramount for achieving full automation and reliability.

In sum, recent research underlines both the impressive progress and the current boundaries of VLM-as-Formalizer pipelines. Task-dependent performance variance—driven primarily by limitations in visual grounding—necessitates ongoing interdisciplinary improvements in multimodal perception, formal specification synthesis, and pipeline modularization. Future efforts must address these open challenges to realize broadly applicable, verifiable, and efficient formalizer systems in complex production and research scenarios.