Text-to-image (T2I) diffusion models excel at generating photorealistic images but often fail to render accurate spatial relationships. We identify two core issues underlying this common failure: 1) the ambiguous nature of data concerning spatial relationships in existing datasets, and 2) the inability of current text encoders to accurately interpret the spatial semantics of input descriptions. We propose CoMPaSS, a versatile framework that enhances spatial understanding in T2I models. It first addresses data ambiguity with the Spatial Constraints-Oriented Pairing (SCOP) data engine, which curates spatially-accurate training data via principled constraints. To leverage these priors, CoMPaSS also introduces the Token ENcoding ORdering (TENOR) module, which preserves crucial token ordering information lost by text encoders, thereby reinforcing the prompt's linguistic structure. Extensive experiments on four popular T2I models (UNet and MMDiT-based) show CoMPaSS sets a new state of the art on key spatial benchmarks, with substantial relative gains on VISOR (+98%), T2I-CompBench Spatial (+67%), and GenEval Position (+131%). Code is available at https://github.com/blurgyy/CoMPaSS.

Developers often seek solutions for their programming problems by retrieving existing questions on technical Q&A sites such as Stack Overflow. In many cases, they fail to find relevant questions due to the knowledge gap between the questions and the queries or feel it hard to choose the desired questions from the returned results due to the lack of explanations about the relevance. In this paper, we propose KGXQR, a knowledge graph based explainable question retrieval approach for programming tasks. It uses BERT-based sentence similarity to retrieve candidate Stack Overflow questions that are relevant to a given query. To bridge the knowledge gap and enhance the performance of question retrieval, it constructs a software development related concept knowledge graph and trains a question relevance prediction model to re-rank the candidate questions. The model is trained based on a combined sentence representation of BERT-based sentence embedding and graph-based concept embedding. To help understand the relevance of the returned Stack Overflow questions, KGXQR further generates explanations based on the association paths between the concepts involved in the query and the Stack Overflow questions. The evaluation shows that KGXQR outperforms the baselines in terms of accuracy, recall, MRR, and MAP and the generated explanations help the users to find the desired questions faster and more accurately.