马树森 题为 “ C3RL: Rethinking the Combination of Channel-independence and Channel-mixing from Representation Learning” 的论文已被《The 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26)》接受发表。该论文摘要如下：

Multivariate time series forecasting has drawn increasing attention due to its practical importance. Existing approaches typically adopt either channel-mixing or channelindependence strategies. While the former struggles to capture variable-specific temporal patterns, the latter improves this aspect but still fails to fully exploit cross-variable dependencies. Hybrid methods based on feature fusion offer limited generalization and interpretability. To address these issues, we propose C3RL, a novel representation learning framework that jointly models both strategies. Motivated by contrastive learning in computer vision, C3RL treats the inputs of the two strategies as transposed views and builds a siamese network architecture: one strategy serves as the backbone, while the other complements it. By jointly optimizing contrastive and prediction losses with adaptive weighting, C3RL balances representation and forecasting performance. Extensive experiments on seven models show that C3RL boosts the best-case performance rate to 81.4% for channel-independence models and to 76.3% for channel-mixing models, demonstrating strong generalization and effectiveness. The code will be available once the paper is accepted.