Large Language Models (LLMs) increasingly serve as research assistants, yet their reliability in scholarly tasks remains under-evaluated. In this work, we introduce PaperAsk, a benchmark that systematically evaluates LLMs across four key research tasks: citation retrieval, content extraction, paper discovery, and claim verification. We evaluate GPT-4o, GPT-5, and Gemini-2.5-Flash under realistic usage conditions, using web interfaces where search operations are opaque to the user. Through controlled experiments, we find consistent reliability failures: citation retrieval fails in 48–98% of multi-reference queries, section-specific content extraction fails in 72–91% of cases, and topical paper discovery yields F1 scores below 0.32, missing over 60% of relevant literature. Further human analysis attributes these failures to the uncontrolled expansion of retrieved context and the tendency of LLMs to prioritize semantically relevant text over task instructions. Across basic tasks, the LLMs display distinct failure behaviors: ChatGPT often withholds responses rather than risk errors, whereas Gemini produces fluent but fabricated answers. To address these issues, we develop lightweight reliability classifiers trained on PaperAsk data to identify unreliable outputs. PaperAsk provides a reproducible and diagnostic framework for advancing the reliability evaluation of LLM-based scholarly assistance systems. The benchmark is publicly available at https://github.com/wuyoscar/PaperAsk.

Geographically weighted regression (GWR) facilitates spatial modeling by providing location-specific coefficients to capture spatial non-stationarity. GWR incorporates a distance decay effect, assigning greater weights to proximal observations under the assumption they exert more influence on the regression parameters. However, distant observations may share significant context similarities, such as socioeconomic or environmental factors, which can influence the regression model. This study introduces an attention-based architecture to address context similarity between samples. A deep learning model termed Context-Attention Geographically Weighted Regression (CatGWR) is proposed to integrate context similarity with distance-based proximity to enhance the estimation of spatial non-stationarity in spatial regression models. Such an integration results in contextualized spatial weights for CatGWR to identify the varying patterns of nonstationary relationships across different spatial locations and context conditions. Validation through simulation experiments and an empirical study on housing prices in Shenzhen, China, shows the superior predictive accuracy and robustness of CatGWR in modeling complex spatial interactions, especially under contextual influences, in which CatGWR improves the R2 of fit and prediction results by at least 6% compared to existing models. Future work will focus on optimizing bandwidth selection and exploring additional attention mechanisms to enhance model performance.

The estimation of spatial heterogeneity within real estate markets holds significant importance in house price modelling. However, employing a single or straightforward distance to measure spatial proximity is probably insufficient in complex urban areas, thereby resulting in an inadequate modelling of spatial heterogeneity. To address this issue, this paper incorporates multiple distance measures within a neural network framework to achieve an optimized measure of spatial proximity (OSP). Consequently, a geographically neural network weighted regression model with optimized measure of spatial proximity (osp-GNNWR) is devised for the purpose of spatially heterogeneous modeling. Trained as a unified model, osp-GNNWR obviates the need for separate pretraining of OSP. This enables OSP to delineate the modeled spatial process through a post hoc calculated value. Through simulation experiments and a real-world case study on house prices, the proposed model reaches more accurate descriptions of diverse spatial processes and exhibits better overall performance. The interpretable results of the case study in Wuhan demonstrate the efficacy of the osp-GNNWR model in addressing spatial heterogeneity within real estate markets, suggesting its potential for modelling and predicting complex geographical phenomena.

A highly linear input buffer is crucial for high-speed and high-resolution analog-to-digital converters (ADCs) since it isolates the kickback noise and package inductance. Several important factors affecting the linearity of the input buffer are analyzed in this brief, and a wideband input buffer with high linearity based on cascade complementary source follower (CCSF) is proposed. This cascaded input buffer is composed of a pMOS source follower (PSF) and an nMOS source follower (NSF). Compensation capacitor, assisted operational amplifier (opamp), bootstrapped-capacitor level-shifting circuit, current amplifier, and optimization strategies are utilized to extend the bandwidth and reduce distortion. Designed in a 65-nm CMOS, the CCSF input buffer achieves the spurious-free dynamic range (SFDR) and a signal-to-noise and distortion ratio (SNDR) of 76.6 and 58.1 dB with 1.9-GHz input frequency, respectively. It occupies 0.0155 mm$^2$and consumes 27 mW at 2.5 V.