Vision Language Model (VLM)-based OCR-free pipelines are increasingly adopted for industrial Key Information Extraction (KIE) systems due to their simpler end-to-end architecture and reduced dependence on potentially noisy OCR outputs. However, their reliability in grounding extracted values to precise bounding boxes remains unclear, raising questions about their suitability for grounding-critical use cases. In this paper, we present a systematic study of bounding box grounding capability in both general and document-specific VLMs. Our findings reveal a consistent extraction-grounding gap across model families. We revisit the OCR system as a grounding primitive and find that delegating the grounding step to Large Language Models (LLMs), rather than VLMs, in a zero-shot manner substantially improves region-level grounding accuracy. We introduce a fine-tuned 0.6B LLM that further improves the predicted box alignment, achieving high overlap with the target field for in-domain data while maintaining low serving latency. These results position OCR-anchored grounding as a practical deployment choice for industrial KIE systems that require both extraction and precise localization.

Despite the success of Large Language Models (LLMs) in structured query generation, OData—a critical RESTful protocol for enterprise APIs—remains under-researched due to a lack of high-fidelity, execution-validated datasets. To bridge this gap, we introduce OmniOData, a framework that generates SynOData, the first large-scale OData corpus featuring execution-grounded queries and reasoning traces. Using this corpus, we develop OmniOData-R1 (1.5B–3B parameters), a family of models that match or surpass frontier proprietary systems, such as GPT-4o and Gemini 3, on realistic industrial benchmarks. Our results demonstrate that the synergy of execution-verified synthetic data and Reinforcement Learning (RL) effectively unlocks the latent reasoning of Small Language Models (SLMs), providing a high-performance, low-latency solution for specialized enterprise query generation.The code and data will be released under an open-source license.