Learning performance modeling has achieved strong predictive performance, but the decision processes of neural models often remain implicit and difficult to analyze. To address this issue, we propose SPARK, a neural-to-symbolic framework for recovering symbolic decision structures from neural predictors of learning performance. SPARK derives performance-related factors from educational interaction data, learns the decision function with a multilayer perceptron as the teacher model, transfers it to a Kolmogorov-Arnold Network (KAN), and then applies structural refinement and symbolic regression to recover compact symbolic expressions. The goal is to convert implicit neural decision behavior into an analyzable symbolic form while preserving its main predictive behavior. Experiments on benchmark educational datasets show that SPARK remains competitive in prediction, preserves most of the teacher model’s decision behavior, and reveals dataset-dependent interaction patterns among student ability, cognition-related factor, and item difficulty.
Artificial intelligence agents are increasingly deployed as professional teammates. However, it remains unclear how they can enhance collaborative performance without hindering human skill development, particularly for novices in high-stakes domains. Cognitive offloading, using external resources (in this case, AI assistants) to reduce one’s own cognitive effort, is central to this tension.We conducted a controlled experiment using a custom platform and the CAIL 2018 dataset. 130 participants were randomly assigned to one of four conditions: a no-AI control, high-offloading AI (which provided direct recommendation), moderate-offloading AI (which provided analytical support), or low-offloading AI (which provided evaluative feedback). The results reveal a fundamental trade-off. The high-offloading condition achieved the highest decision accuracy at 68.8% and the shortest decision time (37.55s). However, it diminished skill development, showing a 7.1% lower accuracy improvement than the control. In contrast, moderate- and low-offloading conditions fostered skill gains of approximately 5.5%. Mediation analysis indicated that this impeded skill development was associated with metacognitive miscalibration (a gap between perceived and actual ability) rather than merely reduced behavioral engagement. These findings extend the “AI Competency Paradox” by revealing a critical offloading threshold below which AI can enhance collaborative performance while supporting skill development, and by identifying metacognitive miscalibration (not reduced effort) as a mechanism underlying skill impairment, suggesting that AI assistants for novices should prioritize adaptive cognitive friction and real-time competence calibration over full automation.
This study examines how the state and the market shape income divisions among paid care workers in China's domestic service economy. We develop the concept of the state-led reproductive boundary to examine how state intervention, through certification, training, work arrangement, and long-term care insurance, redraws the lines along which reproductive labour is valued, financed, and protected. Drawing on a 2021 survey of 919 domestic workers in Shanghai, the first Chinese city to implement a municipality-wide long-term care insurance scheme, we distinguish maternity-infant care, eldercare, and non-nurturant work, and model both monthly and hourly income. We compare a purely cultural account of care valuation with one that incorporates the state. Cultural valuation alone cannot explain the wage divisions within paid care. The division between nurturant and non-nurturant work in China is organised less through race than through human capital, certification, and market recognition. Within nurturant work, the maternity-infant care advantage holds in monthly but not hourly terms, since live-in arrangements obscure the low hourly returns of feminised, time-intensive care. The culturally anticipated eldercare penalty does not materialise. Eldercare workers absorbed into the hourly-priced, state-financed insurance scheme are no longer disadvantaged relative to non-nurturant workers, showing that state intervention can partially insulate a culturally devalued form of care from market discounting.
Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts. Recently, large language models (LLMs) have demonstrated the capacity to achieve competitive SciNER performance with minimal human effort. Existing research highlights the importance of incorporating candidate entity type information for accurate entity recognition and classification by LLMs. However, when too many candidate entity types are provided in the prompt, LLMs struggle to accurately recognize and label entities in scientific texts, where entity types are more complex than in general domains. To address this challenge, we propose TdSciNER, a type-driven approach that effectively leverages entity type information to enhance SciNER performance. In TdSciNER, we first design an entity type filter model to identify the most likely entity types present in a given sentence. Subsequently, we introduce an auxiliary multi-class entity typing task within a multi-task learning framework alongside SciNER to obtain richer contextual representations. Then, we develop a novel demonstration selection strategy based on sentence similarity and entity type diversity to activate the in-context learning capabilities of LLMs, thereby improving entity recognition accuracy across diverse scientific domains. Experiments on three datasets demonstrate that our method achieves performance comparable to fully supervised models. Further analysis validates that each entity type-driven component in TdSciNER contributes to the improvement of SciNER performance. This work provides valuable insights for future advancements in SciNER and broader information extraction tasks in scientific text mining.
This study analyzes and contrasts different phenomenological methods used to model the nuclear equation of state (EOS) for neutron star matter based on covariant energy density functionals (CEDFs). Using two complementary methodologies, we seek to capture a comprehensive picture of the potential behaviors of ultradense nucleonic matter and identify the most plausible models based on current observational and experimental constraints. Observational data from radio pulsar timing, gravitational-wave detection of GW170817, and X-ray timing provide critical benchmarks for testing the models. We have derived the EOS posteriors for various CEDF models within the CompactObject package, utilizing recent observational data on neutron stars, state-of-the-art theoretical constraints from calculations in chiral effective field theory for pure neutron matter at low densities, and pQCD-derived constraints. Our analysis has demonstrated that while all considered CEDF models broadly reproduce current astrophysical and theoretical constraints, subtle yet important differences persist among them, with each framework exhibiting distinct characteristics at supranuclear density. This is particularly true for the proton fraction inside neutron stars, but is also supported by the models' behavior with respect to the pure neutron matter EOS and the density dependence of the speed of sound. Our study highlights the sensitivity of predictions for dense matter to the underlying EOS parameterizations and the priors considered.
