Many decision-making tasks, where both accuracy and efficiency matter, still require human supervision. For example, tasks like traffic officers reviewing hour-long dashcam footage or researchers screening conference videos can benefit from concise summaries that reduce cognitive load and save time. Yet current vision-language models (VLMs) often produce verbose, redundant outputs that hinder task performance. Existing video caption evaluation depends on costly human annotations and overlooks the summaries' utility in downstream tasks. We address these gaps with $\underline{\textbf{V}}$ideo-to-text $\underline{\textbf{I}}$nformation $\underline{\textbf{B}}$ottleneck $\underline{\textbf{E}}$valuation (VIBE), an annotation-free method that scores VLM outputs using two metrics: $\textit{grounding}$ (how well the summary aligns with visual content) and $\textit{utility}$ (how informative it is for the task). VIBE selects from randomly sampled VLM outputs by ranking them according to the two scores to support effective human decision-making. Human studies on $\texttt{LearningPaper24}$, $\texttt{SUTD-TrafficQA}$, and $\texttt{LongVideoBench}$ show that summaries selected by VIBE consistently improve performance—boosting task accuracy by up to $61.23$% and reducing response time by $75.77$% compared to naive VLM summaries or raw video.

Achieving Zero-carbonizing electricity growth is a priority decarbonization issue. Multi-pronged policies such as TGC, RPS, and CET have been implemented in the power sector. How to deal with the different pressures generated by the above policies in minimizing costs at the same time is an important issue worth discussing. This paper constructs the framework that includes TGC futures trading, carbon emissions trading, and inter-provincial power trading under the quota system. Specifically, we construct the realization cost function of the RPS target, carbon cap and electricity demand, divide the provinces into buyers and suppliers, optimize the power generation, and distribute the gains based on the Shapley Value method. A simulation analysis of four Chinese provinces showed that each province has gained more benefits and less carbon emissions from inter-province trading, resulting in a win-win situation. Those conclusions provide some insightful references for the future development of power market design and decarbonization.

Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanations are contrastive, explaining not just why one action is taken, but why one action is taken instead of another. We formalize the notion of contrastive explanations for robotic planning policies based on Markov decision processes, drawing on insights from the social sciences. We present methods for the automated generation of contrastive explanations with three key factors: selectiveness, constrictiveness and responsibility. The results of a user study with 100 participants on the Amazon Mechanical Turk platform show that our generated contrastive explanations can help to increase users' understanding and trust of robotic planning policies, while reducing users' cognitive burden.