Complex table question answering (TQA) remains challenging, as real-world table, usually designed for human readability with multi-level headers and fragmented hierarchical semantics, largely hindering large language models (LLMs) from accurately aligning conditions, attributes, and values during reasoning. Existing approaches typically rely on handcrafted table linearization or prompts, forcing LLMs to infer header hierarchies, which frequently leads to brittle reasoning and hallucinations. To this end, we propose SMART, a unified framework that explicitly decouples table structure understanding from reasoning execution. SMART consists of three components: Semantic Header Flattening for converting multi-level headers into explicit single-level descriptors, Global Understanding for capturing holistic table–question semantics, and Pseudo-Code-Style Reasoning for structured, step-by-step inference with external validation. Extensive experiments on multiple benchmarks demonstrate that SMART substantially improves both the accuracy and robustness of complex TQA, achieving state-of-the-art performance.

Multimodal Sentiment Analysis (MSA) often suffers from performance degradation due to missing modalities in practical applications. Existing methods typically focus on feature completion but neglect semantic shifts caused by distribution gaps and decision risks under high uncertainty. In this paper, we propose a Distributional Error-Aware Reliability (DEAR) estimation framework for robust MSA. Specifically, we design a Hierarchical Distribution-Constrained Reconstruction (HDCR) module to mitigate semantic shifts by explicitly aligning reconstructed features with the original distributional manifold. Meanwhile, a reliability evaluation module (SURE) is introduced to quantitatively measure reconstruction fidelity. By perceiving inherent uncertainty, SURE provides a reliability-driven gating mechanism for the Synergistic-Robust Dual-Stream (SR-DS) architecture. This mechanism enables the model to dynamically adjust contribution weights: strengthening cross-modal synergistic effects when data fidelity is high, while shifting focus toward robust paths under high-risk missingness to safeguard performance. Extensive experiments on MOSI, MOSEI, and SIMS datasets validate the effectiveness and decision reliability of DEAR.