See, Symbolize, Act: Grounding VLMs with Spatial Representations for Better Gameplay
arXiv:2603.11601v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at describing visual scenes, yet struggle to translate perception into precise, grounded actions. We investigate whether providing VLMs with both the visual frame and the symbolic representation of the scene can improve their performance in interactive environments. We evaluate three state-of-the-art VLMs across Atari games, VizDoom, and AI2-THOR, comparing frame-only, frame with self-extracted symbols, frame with ground-truth symbols, and symbol-only pipelines. Our results indicate that all models benefit when the symbolic information is accurate. However, when VLMs extract symbols themselves, performance becomes dependent on model capability and scene complexity. We further investigate how accurately VLMs can extract symbolic information from visual inputs and how noise in these symbols affects decision-making and gameplay performance. Our findings reveal that symbolic grounding is beneficial in VLMs
arXiv:2603.11601v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at describing visual scenes, yet struggle to translate perception into precise, grounded actions. We investigate whether providing VLMs with both the visual frame and the symbolic representation of the scene can improve their performance in interactive environments. We evaluate three state-of-the-art VLMs across Atari games, VizDoom, and AI2-THOR, comparing frame-only, frame with self-extracted symbols, frame with ground-truth symbols, and symbol-only pipelines. Our results indicate that all models benefit when the symbolic information is accurate. However, when VLMs extract symbols themselves, performance becomes dependent on model capability and scene complexity. We further investigate how accurately VLMs can extract symbolic information from visual inputs and how noise in these symbols affects decision-making and gameplay performance. Our findings reveal that symbolic grounding is beneficial in VLMs only when symbol extraction is reliable, and highlight perception quality as a central bottleneck for future VLM-based agents.
Executive Summary
This study investigates the impact of incorporating symbolic representations alongside visual frames in Vision-Language Models (VLMs) to enhance performance in interactive environments. Evaluating three state-of-the-art VLMs across Atari, VizDoom, and AI2-THOR, the researchers compare combinations of frame-only, frame with self-extracted symbols, frame with ground-truth symbols, and symbol-only pipelines. The findings indicate that accurate symbolic information improves VLM performance across all models, but reliance on self-extracted symbols introduces variability dependent on model capability and scene complexity. The research highlights that symbolic grounding enhances VLM efficacy only when symbol extraction is reliable, positioning perception quality as a critical bottleneck for future agent development. The work underscores the importance of reliable symbolic extraction as a prerequisite for meaningful integration of symbolic information in VLM-based systems.
Key Points
- ▸ Symbolic information improves VLM performance when accurate
- ▸ Self-extracted symbols introduce performance variability based on model capability and scene complexity
- ▸ Reliable symbolic extraction is a prerequisite for effective symbolic grounding in VLMs
Merits
Empirical Validation
The study provides robust empirical evidence across multiple platforms (Atari, VizDoom, AI2-THOR) demonstrating the benefit of accurate symbolic information across diverse VLM architectures.
Demerits
Limitation of Self-Extraction
The dependency on self-extracted symbols introduces inconsistency, particularly in complex or ambiguous scenes where extraction accuracy is compromised, limiting scalability and generalizability.
Expert Commentary
The paper makes a valuable contribution by empirically disentangling the role of symbolic grounding in VLMs, particularly in interactive environments. While the benefits of accurate symbolic information are clear, the nuanced finding that self-extracted symbols introduce dependency on model capability and scene complexity is particularly insightful. This challenges the prevailing assumption that embedding symbolic representations is inherently advantageous. Instead, it positions the reliability of extraction as a critical conditional factor. The work aligns with broader trends in embodied AI, where perception-action coupling remains a persistent challenge. Notably, the authors’ emphasis on perception quality as a central bottleneck is a timely reminder that technological advances in modeling must be matched with advances in input fidelity. This study should inform both academic trajectories and industry R&D strategies, especially for applications in gaming, robotics, and assistive technologies where precise action grounding is paramount.
Recommendations
- ✓ 1. Incorporate or mandate pre-grounded symbolic data in VLM deployment pipelines for interactive applications to mitigate variability caused by self-extraction.
- ✓ 2. Invest in hybrid architectures that combine visual, symbolic, and contextual modalities with adaptive extraction mechanisms to improve reliability and scalability.