Text-to-image diffusion models achieve remarkable generation quality, yet their internal mechanisms for grounding prompt semantics into visual structure remain poorly understood. We present a novel mechanistic interpretability framework for Stable Diffusion that probes how individual prompt tokens are represented and utilized during the denoising process. Given a prompt, we record cross-attention activations throughout UNet denoising and convert them into token-level spatial grounding maps that indicate where each token contributes signal during image synthesis. To establish causal faithfulness, we perform controlled prompt interventions by removing a single word at a time while keeping the sampling seed fixed, producing counterfactual generations. To quantify mechanistic sensitivity, we introduce a head-resolved spike score based on divergence between per-head token contribution distributions before and after intervention, enabling module-wise and head-wise attribution of semantic changes. Experiments on compositional prompts and challenging relational descriptions reveal systematic patterns of token grounding, semantic drift, and head specialization across denoising timesteps. Our results provide a practical and reproducible toolkit for analyzing how diffusion models encode and apply semantic information, supporting deeper transparency in text-to-image generation.

Logical Table-to-Text (LT2T) generation aims to produce natural-language sentences that are logically faithful to structured tabular data. While recent Large Language Models (LLMs) show high performance on aggregate fidelity metrics, these scores provide only a coarse view of performance, obscuring specific logic-type reasoning failures and models’ meta-logical awareness. We propose an operation-aware diagnostic framework that evaluates four core competencies: (1) Logical Form (LF) execution accuracy, (2) fidelity of LF-conditioned generation, (3) logic-type identification, and (4) LF-free generation.We apply this framework to a suite of frontier LLMs and perform fine-grained analysis across logic types such as aggregation, ordinal, and superlative reasoning. Our results show that LT2T fidelity assessment can be unstable; the choice of verifier and logic type can substantially alter conclusions and model rankings. Crucially, we identify a meta-logical gap: models often generate faithful statements while failing to identify the underlying operation.

Logical Table-to-Text (LT2T) generation requires models to both verbalize tabular data and reason over it - performing comparisons, aggregations, and causal inference. While many generation tasks struggle with similar analytical demands, LT2T provides a structured perspective on reasoning capabilities in natural language generation. This survey uses LT2T as a lens to focus on reasoning in data-to-text tasks. By focusing narrowly on LT2T, we present a deep taxonomy of methods that inject, structure, or verify reasoning steps, allowing a level of technical granularity missing in broader surveys. We review representative models and evaluation metrics, and highlight how LT2T techniques transfer to general generation challenges involving logic, numeracy, and faithfulness. Our goal is to distill lessons from LT2T that apply more widely, while also guiding future research in table-based reasoning.