SKG-VLA: Scene Knowledge Graph Priors for Structured Scene Semantics and Multimodal Reasoning for Decision Making

Researchers present SKG-VLA, an AI system that uses Scene Knowledge Graphs to improve decision-making in large-scale complaint handling by integrating multimodal evidence (text, images, metadata) with structured reasoning about entities, policies, and temporal events. The approach demonstrates improved accuracy and robustness across policy-grounded reasoning and long-tail scenarios.

This research addresses a practical yet underexplored challenge in enterprise AI systems: making defensible decisions in complaint handling at scale. Traditional approaches rely on shallow classification or template matching across isolated data sources, missing the interconnected nature of real complaint scenarios. SKG-VLA introduces structured knowledge graphs to encode complaint entities, policy rules, temporal sequences, and cross-evidence dependencies into a unified representation, enabling more sophisticated reasoning.

The work represents a broader shift in AI development from isolated modality processing toward integrated, context-aware systems that incorporate domain knowledge and regulatory constraints. In complaint handling systems used by major platforms, this capability directly reduces false positives, improves policy compliance, and handles edge cases that simpler models miss. The three-stage training strategy—domain adaptation, instruction tuning, and multimodal alignment—reflects current best practices for injecting domain-specific reasoning into large language and vision models.

For enterprise AI deployments, particularly in regulated industries like e-commerce and fintech, this approach offers measurable improvements in handling ambiguous situations with incomplete evidence. The dataset and methodology also establish benchmarks for evaluating multimodal reasoning in structured domains. Long-tail performance improvements matter significantly in production systems where rare but high-impact complaint types often escape adequate handling.

Future development likely focuses on scaling these graph-based reasoning approaches to real-time decision systems and integrating explanability mechanisms that justify decisions to stakeholders. As complaint volumes grow and regulatory scrutiny increases, systems combining structured semantics with multimodal reasoning become increasingly valuable for maintaining trust and compliance.

• →Scene Knowledge Graphs enable structured reasoning over heterogeneous complaint evidence by representing entities, policies, events, and dependencies in unified representations.
• →The three-stage training approach (domain pre-training, task fine-tuning, multimodal alignment) consistently improves policy compliance and decision accuracy.
• →Long-tail generalization and robustness under incomplete evidence demonstrate practical value for real-world complaint handling systems.
• →Integration of explicit rule knowledge and temporal reasoning outperforms shallow classification methods on policy-grounded decision tasks.
• →The research establishes benchmarks for evaluating multimodal reasoning in structured, regulated domains beyond generic vision-language tasks.