Search Results for « Emergent abilities »

The success of foundation models in language and vision rests on a simple empirical promise: predictable improvements, and occasionally qualitatively new capabilities, emerge as models, data, and compute are scaled together. Single-cell foundation models (scFMs)-transformers pretrained by self-supervision on tens of millions of single-cell transcriptomes-were built on the implicit assumption that the same promise transfers to biology. This review asks whether that assumption holds: can scaling actually make scFMs smarter, and if so, smarter in what sense? We distinguish four notions of "smarter" that the literature conflates-downstream task accuracy, zero-shot generalization, biological knowledge captured in representations, and the richness of internal computation-and review the evidence for each. On the optimistic side, controlled scaling studies on masked-reconstruction transformers show clean power-law improvements in reconstruction loss, and large generative cell-language models exhibit emergent-looking gains on complex tasks. On the skeptical side, multiple independent evaluations find that zero-shot scFM embeddings are frequently matched or beaten by classical pipelines and simple linear baselines, that benefits saturate well below language-model scales, and that data redundancy rather than data volume governs returns. We argue that the single-cell setting differs from natural language in ways-bounded biological information, heavy technical noise, and a low effective-dimensionality ceiling-that make naive extrapolation of language scaling laws unsafe. Mechanistic-interpretability evidence suggests that scale may reorganize how scFMs compute (toward more biologically coherent, convergent circuits and more interpretable features) even when leaderboard metrics move little, implying that "smarter" should be measured by the structure of learned representations and not only by benchmark accuracy. We close with concrete recommendations for compute-optimal pretraining, evaluation beyond saturated benchmarks, and the role of interpretability as a scaling diagnostic.

This article explores psychological factors influencing schoolchildren's career choices and ability development. The study aims to identify how motivation, personal traits, and social context shape students’ professional orientation. Using a qualitative analytical method, the research draws from psychological and pedagogical literature, incorporating comparative and theoretical analysis. Results show that self-awareness, family influence, and social stereotypes significantly affect students’ career decisions, highlighting the need for integrated career guidance approaches involving schools, families, and society.

The scale-up of AI models for analyzing largescale power systems necessitates a thorough understanding of their scaling properties. Existing studies on these properties provide only partial insights, showing that loss function decreases predictably with increased model scales; yet no scaling law for power system AI models has been established, and model performance remains unpredictable due to “emergent abilities”. This study pioneers the discussion on the emergent abilities of graph neural network (GNN) for analyzing large-scale power systems, revealing that model performance improves dramatically once model scale exceeds a threshold. Furthermore, we introduce an empirical power-law formula to quantify the relationship between this threshold and the power system size. Our theory accurately predicts the threshold for the appearance of emergent ability in large-scale power systems, including a synthetic 10,000- bus and a real-world 19,402-bus systems.

Preschool teachers’ pedagogical cognition and instructional support of emergent writing significantly shape the development of children's emergent writing abilities. In the linguistically complex border regions of China, where 3–6-year-old children often lack familial cultural capital, preschool teachers’ emergent writing cognition and instructional support play an even more critical role in shaping children's emergent writing abilities development. This study examined children's emergent writing abilities and teachers’ emergent writing cognition and instructional support through standardized assessments and questionnaires, involving 250 children (aged 3–6 years) from 55 classrooms and 110 preschool teachers in China's border regions. The study identified three distinct profiles of preschool teachers’ emergent writing cognition and instructional support: (1) cognitively underdeveloped class; (2) cognition-preceded support class; and (3) moderately balanced class. Children's emergent writing abilities showed significant positive correlations with both teachers’ emergent writing cognition and instructional support. Notably, the association between preschool teachers’ emergent writing cognition and children's emergent writing outcomes was stronger than that between emergent writing support and children's emergent writing. Preschool teachers in China's border regions should establish scientifically grounded conceptions of emergent writing, increase research-based instructional support, and effectively translate pedagogical knowledge into supportive behaviors to achieve cognition–support alignment, thereby promoting children's emergent writing abilities.

Objective: There is a significant negative relation between externalizing behavior and emergent literacy skills among preschool children. Method: The purpose of this study was to examine the impact of gender on the predictive relation of externalizing behavior and emergent literacy in a group of 178 preschool children (mean age = 48.50 months, SD = 3.66; 48% boys). Results: Externalizing behaviors predicted emergent literacy over time. Distinct patterns of predictive associations dependent on gender were found. Girls with higher levels of externalizing behaviors experienced less change in their vocabulary skills compared with the vocabulary change shown by girls with lower levels of these problem behaviors. Conclusion: The results suggest that early identification programs that include externalizing behavior problems and their relation with emergent literacy development should account for potential gender differences. A theoretical framework in which girls with behavior problems receive less opportunity for vocabulary acquisition is presented.