DPANet: Dual Pyramid Attention Network for Multivariate Time Series Forecasting

We conducted rigorous ablation studies to validate DPANet's key components (Table \ref{tab:ablation-study}). The full model consistently outperforms all variants. To test our dual-domain hypothesis, we designed two specialized versions: a Temporal-Only model (fusing two identical temporal pyramids) and a Frequency-Only model (fusing two spectral pyramids). Both variants underperformed significantly, confirming that the fusion of heterogeneous temporal and frequency information is critical. Furthermore, replacing the cross-attention mechanism with a simpler method (w/o Cross-Fusion) caused the most severe performance degradation. This result underscores that our interactive fusion block is the most essential component.

Adaptive Rectification Sampling for Test-Time Compute Scaling

The newly released OpenAI-o1 and DeepSeek-R1 have demonstrated that test-time scaling can significantly improve model performance, especially in complex tasks such as logical reasoning. Common test-time scaling methods involve generating more chain of thoughts (CoTs) or longer CoTs with self-correction. However, while self-correction can improve performance, it may lead to significant token waste and reduce readability of the CoT if the reasoning steps are already correct. To demonstrate that large language models (LLMs) can rectify errors at a more fine-grained level, we propose Adaptive Rectification Sampling (AR-Sampling), which can guide the LLMs to self-correction at the appropriate step. AR-Sampling leverages a process-supervised reward model (PRM) as a verifier and constructed trigger sentences to guide the model in adaptive step-level rethinking. Through the experiments on GSM8K and MATH500, it indicate that our approach enables the models to rethink in more fine-grained level, improving the accuracy of solutions, while generating a reasonable number of additional tokens.