Natural language to SQL (NL2SQL) provides an intuitive interface for querying structured data, yet real user questions are often noisy, ambiguous, and weakly grounded to database semantics.As a result, token-level schema linking and single-pass SQL decoding can be brittle: small misunderstandings in language or schema grounding may propagate into incorrect generation.We present QBridge, an agentic, feedback-driven NL2SQL framework based on a Refined Gold Query Paradigm, which bridges natural language and SQL via Gold Query—a structured, SQL-aligned intermediate representation.A core insight of QBridge is Distilled Back-Translation (DBT) for SL-independent rewriting.DBT converts SQL-grounded supervision into execution-verified Gold-Query-style rewrites from a teacher model, and distills a lightweight, plug-and-play rewriter that generates schema-aware rewrites without requiring explicit schema linking at inference.QBridge then (i) verifies and conservatively refines the rewrite into a high-fidelity Refined Gold Query, and (ii) refines the generated SQL with dual feedback from execution validity and semantic consistency, enabling interpretable self-correction while remaining compatible with diverse SQL backbones.Extensive experiments on Spider, BIRD, and three robustness variants demonstrate that QBridge consistently improves zero-shot NL2SQL, outperforming strong prompting and agentic baselines while showing strong robustness and generalization. Code and data are available at https://github.com/WannaBSteve/QBridge.

Multimodal learning is garnering significant attention for its capacity to represent diverse human perceptions (e.g., linguistic, acoustic, and visual signals), achieving more natural and intuitive interactions with technology.However, the frequent occurrence of incomplete data, either within a single modality (intra-modality) or across different modalities (inter-modality), presents substantial challenges in reliable semantic interpretation and model reasoning.Furthermore, there is currently no robust representation learning mechanism capable of managing both intra-modality and inter-modality real-data deficiencies.To address this challenge, we present T²DR, a two-tier deficiency-resistant framework for incomplete multimodal learning, which comprises two main modules:(1) Intra-Modal Deficiency-Resistant module (IADR): To address fine-grained deficiencies, we introduce Intra-Attn to focus on the available data while avoiding excessive suppression of the missing regions.(2) Inter-Modal Deficiency-Resistant module (IEDR): To handle coarse-grained deficiencies, we propose the shared feature prediction (SFP) to leverage cross-modal shared features for preliminary data imputation. Subsequently, we apply Inter-Attn to allocate appropriate attention to each modality based on the results from the capability-aware scorer (CAS).Extensive experiments are performed on two well-known multimodal benchmarks, CMU-MOSI and CMU-MOSEI, across various missing scenarios for sentiment analysis. Experimental results show that T²DR significantly outperforms the SOTA models. Code is available at https://github.com/LH019/T2DR.