DynaQuery: A Self-Adapting Framework for Querying Structured and Multimodal Data

• URL: http://arxiv.org/abs/2510.18029v1
• Date: Mon, 20 Oct 2025 19:02:35 GMT
• Title: DynaQuery: A Self-Adapting Framework for Querying Structured and Multimodal Data
• Authors: Aymane Hassini,
• Abstract summary: We present DynaQuery, a unified, self-adapting framework for querying unstructured data.<n>At the heart of DynaQuery lies the Introspection and Linking Engine (SILE), a novel systems primitive that elevates schema linking to a first-class query planning phase.<n>Our findings provide a validated architectural basis for developing natural language database interfaces that are robust, adaptable, and predictably consistent.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of Large Language Models (LLMs) has accelerated the long-standing goal of enabling natural language querying over complex, hybrid databases. Yet, this ambition exposes a dual challenge: reasoning jointly over structured, multi-relational schemas and the semantic content of linked unstructured assets. To overcome this, we present DynaQuery - a unified, self-adapting framework that serves as a practical blueprint for next-generation "Unbound Databases." At the heart of DynaQuery lies the Schema Introspection and Linking Engine (SILE), a novel systems primitive that elevates schema linking to a first-class query planning phase. We conduct a rigorous, multi-benchmark empirical evaluation of this structure-aware architecture against the prevalent unstructured Retrieval-Augmented Generation (RAG) paradigm. Our results demonstrate that the unstructured retrieval paradigm is architecturally susceptible to catastrophic contextual failures, such as SCHEMA_HALLUCINATION, leading to unreliable query generation. In contrast, our SILE-based design establishes a substantially more robust foundation, nearly eliminating this failure mode. Moreover, end-to-end validation on a complex, newly curated benchmark uncovers a key generalization principle: the transition from pure schema-awareness to holistic semantics-awareness. Taken together, our findings provide a validated architectural basis for developing natural language database interfaces that are robust, adaptable, and predictably consistent.

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