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DeSQ is a decomposition-based framework for generating SPARQL queries from natural language questions. It breaks complex questions into atomic constraints, maps them to SPARQL fragments, and assembles them into complete queries, outperforming state-of-the-art on four out of five benchmarks.
The paper decomposes the attention interaction matrix into routing (skew-symmetric) and filtering (symmetric) components, introducing S-D attention to disentangle them. It reveals a spectral cascade in routing that predicts where attention can be simplified, achieving significant parameter reduction with minimal perplexity loss.
Proposes PESD-TSF, a physics-inspired structured decomposition framework for long-term time series forecasting that addresses periodic perception degradation, trend-noise entanglement, and loss of cross-variable dependencies via multiplicative periodic gating, multi-scale structured encoder, and cross-scale collaborative attention.