Y. B. Xu

Tingwei Cao, Yan Xu

Microgrid operation is highly vulnerable to short-term load uncertainty, while conventional predict-then-optimize pipelines cannot fully align probabilistic forecasting quality with downstream robust scheduling performance. This paper proposes a CVaR-guided decision-focused learning and risk-triggered re-optimization framework for two-stage robust microgrid operation. A probabilistic load forecasting model first generates multi-quantile outputs, which are converted into prediction intervals to parameterize the load uncertainty set of the downstream two-stage robust optimization (TSRO) model. To improve forecasting reliability under difficult and high-risk operating conditions, a CVaR-guided forecasting objective is introduced to emphasize tail-sensitive samples. To further close the forecast-decision gap, a convex regularized surrogate TSRO model and a smooth regret loss are developed, enabling downstream operational feedback to be propagated to the forecasting model through KKT-based implicit differentiation. For online deployment, a risk-triggered re-optimization mechanism selectively re-solves the remaining-horizon TSRO only when the schedule mismatch becomes significant, avoiding unnecessary online computation. Case studies on modified IEEE 33-bus and 69-bus microgrids demonstrate superior probabilistic forecasting accuracy, operational economy, and tail-risk mitigation over benchmark methods, while preserving near-full-re-optimization performance with less than 0.5% higher operating cost and up to 91% lower daily solution time.

Wanlong Liu, Junxiao Xu, Fei Yu et al.

Recent advancements in Long Chain-of-Thought (CoT) reasoning models have improved performance on complex tasks, but they suffer from overthinking, which generates redundant reasoning steps, especially for simple questions. This paper revisits the reasoning patterns of Long and Short CoT models, observing that the Short CoT patterns offer concise reasoning efficiently, while the Long CoT patterns excel in challenging scenarios where the Short CoT patterns struggle. To enable models to leverage both patterns, we propose Question-Free Fine-Tuning (QFFT), a fine-tuning approach that removes the input question during training and learns exclusively from Long CoT responses. This approach enables the model to adaptively employ both reasoning patterns: it prioritizes the Short CoT patterns and activates the Long CoT patterns only when necessary. Experiments on various mathematical datasets demonstrate that QFFT reduces average response length by more than 50\%, while achieving performance comparable to Supervised Fine-Tuning (SFT). Additionally, QFFT exhibits superior performance compared to SFT in noisy, out-of-domain, and low-resource scenarios.