Let B be the maximum difference betweenU1t and U2t, and let (π,θ1,θ2) be a Nash Equilibrium forG. Let π1 be the best response to the first teacher (with utilityU1t) and let π1+2 be the best response policy to the joint teacher. This result shows that as we reduce the number of random episodes, the approximation to aminimax regret strategy improves. Let G be the dual curriculum game in which the first teacher maximizes regret, so U1t = URt, and the second teacher plays randomly, soU2t = UUt . Finally,we need to show thatπ2+3 isoptimal for the student.

The notoriously challenging book is being re-released for its 30th anniversary. Its fandom is annoying, sure--but at least they read. The host had been grilling Wallace, ostensibly invited on to discuss his own literary and journalistic output, on range of topics: tennis, teaching, why women don't like Westerns, depression, and, yes, Anthony Minghella's Academy Award-winning epic war drama, which had by the time the interview aired already become a punch line . Watching the interview, it's clear Wallace, who died by suicide in 2008, bristles at being pressed to purvey rank punditry on the popular culture at large like some kind of dancing monkey. But the exercise revealed how Rose, and large swaths of American intellectual culture circa the late-1990s, thought of Wallace.

Two plays soaked in technological anxiety. "An Ark" resembles a webinar with a staring contest, one that no human can win. Before you enter "An Ark," a "mixed reality" performance at the Shed, you check your coat and, more oddly, your shoes. Inside, there are three concentric circles of chairs arranged on a red carpet and, overhead, a white globe resembling a hot-air balloon. A docent explained that, through my virtual-reality headset, I would see four more chairs--and, ideally, they shouldn't float.

Neural Information Processing Systems http://nips.cc/

News articles often reference numerous organizations, but traditional Named Entity Recognition (NER) treats all mentions equally, obscuring which entities genuinely drive the narrative. This limits downstream tasks that rely on understanding event salience, influence, or narrative focus. We introduce Protagonist Entity Recognition (PER), a task that identifies the organizations that anchor a news story and shape its main developments. To validate PER, we compare the predictions of Large Language Models (LLMs) against annotations from four expert annotators over a gold corpus, establishing both inter-annotator consistency and human-LLM agreement. Leveraging these findings, we use state-of-the-art LLMs to automatically label large-scale news collections through NER-guided prompting, generating scalable, high-quality supervision. We then evaluate whether other LLMs, given reduced context and without explicit candidate guidance, can still infer the correct protagonists. Our results demonstrate that PER is a feasible and meaningful extension to narrative-centered information extraction, and that guided LLMs can approximate human judgments of narrative importance at scale.

Creativity is often seen as a hallmark of human intelligence. While large language models (LLMs) are increasingly perceived as producing creative text, there is still no holistic framework to evaluate their creativity across diverse scenarios. Existing evaluation methods remain fragmented, with dramatic variation across domains and tasks, largely due to differing definitions and measurements of creativity. Inspired by the hypothesis that creativity is not one fixed idea, we propose CreativityPrism, an evaluation analysis framework that decomposes creativity into three dimensions: quality, novelty, and diversity. CreativityPrism incorporates nine tasks, three domains, i.e., divergent thinking, creative writing, and logical reasoning, and twenty evaluation metrics, which measure each dimension in task-specific, unique ways. We evaluate 17 state-of-the-art (SoTA) proprietary and open-sourced LLMs on CreativityPrism and analyze the performance correlations among different metrics and task domains. Our results reveal a notable gap between proprietary and open-source models. Overall, model performance tends to be highly correlated across tasks within the same domain and less so across different domains. Among evaluation dimensions, diversity and quality metrics show strong correlations - models that perform well on one often excel on the other - whereas novelty exhibits much weaker correlation with either. These findings support our hypothesis that strong performance in one creativity task or dimension does not necessarily generalize to others, underscoring the need for a holistic evaluation of LLM creativity.