Table Retrieval (TR) has traditionally been formulated as an ad-hoc retrieval problem, where relevance is primarily determined by topical semantic similarity. With the growing adoption of LLM-based agentic systems, access to structured data is increasingly instruction-driven, where relevance is conditional on explicit content and schema constraints rather than topical similarity alone. We therefore formalize Instruction-Following Table Retrieval (IFTR), a new task that requires models to jointly satisfy topical relevance and fine-grained instruction constraints. We identify two core challenges in IFTR: (i) sensitivity to content scope, such as inclusion and exclusion constraints, and (ii) awareness of schema-grounded requirements, including column semantics and representation granularity--capabilities largely absent in existing retrievers. To support systematic evaluation, we introduce FollowTable, the first large-scale benchmark for IFTR, constructed via a taxonomy-driven annotation pipeline. We further propose a new metric, termed the Instruction Responsiveness Score, to evaluate whether retrieval rankings consistently adapt to user instructions relative to a topic-only baseline. Our results indicate that existing retrieval models struggle to follow fine-grained instructions over tabular data. In particular, they exhibit systematic biases toward surface-level semantic cues and remain limited in handling schema-grounded constraints, highlighting substantial room for future improvements.

The humanization of antibodies (Abs) remains one of the main pathways for therapeutic antibody development. With the advantages of diffusion models, here we present HuAbDiffusion, a discrete language diffusion model used for antibody humanization by generating humanized antibodies from scratch. HuAbDiffusion starts from three complementary determinant regions (CDRs) and finally generate whole V region sequences. The model was evaluated on 22 mAbs and compared with several existing methods, the test results show the effectiveness and better performance of the proposed model. Besides, the potential optimal humanized antibodies to be selected could be narrowed down to a reasonable level with the usage of pretrained language models. The most significant is that the binding affinity of the humanized antibody can be retained or even increased generated by HuAbDiffusion. The method can be reached out through our previous established YabXnization server at https://www.genscript.com/tools/yabxnization-service.

The supervision of novel psychoactive substances (NPSs) is a global problem, and the regulation of NPSs was heavily relied on identifying structural matches in established NPSs databases. However, violators could circumvent legal oversight by altering the side chain structure of recognized NPSs and the existing methods cannot overcome the inaccuracy and lag of supervision. In this study, we propose a scaffold and transformer-based NPS generation and Screening (STNGS) framework to systematically identify and evaluate potential NPSs. A scaffold-based generative model and a rank function with four parts are contained by our framework. Our generative model shows excellent performance in the design and optimization of general molecules and NPS-like molecules by chemical space analysis and property distribution analysis. The rank function includes synthetic accessibility score and frequency score, as well as confidence score and affinity score evaluated by a neural network, which enables the precise positioning of potential NPSs. Applied STNGS framework with molecular docking and a G protein-coupled receptor (GPCR) activation-based sensor (GRAB), we successfully identify three novel synthetic cannabinoids with activity. STNGS constrains the chemical space to generate NPS-like molecules database with diversity and novelty, which assists in the ex-ante regulation of NPSs.

Abstract Insulator defect detection is crucial for the stable operation of power systems. It has become a mainstream research direction to realise insulator defect detection based on the combination of line images captured by UAVs and deep learning techniques. However, the existing high‐quality insulator defect detection models still face problems such as relying on massive‐labelled data and huge model parameters. Especially on resource‐constrained devices, it becomes a challenge to strike a balance between model lightweighting and performance. Although the knowledge distillation technique provides a solution for model lightweighting, the loss of information in the distillation process leads to the performance degradation of small models, which in turn creates a paradox between lightweighting and performance. Hence, an insulator defect detection method based on federated knowledge distillation is proposed. The method not only realises the lightweighting of the model, but also effectively improves the model performance by collaboratively training the model through the federated learning approach. Moreover, the asynchronous aggregation approach and model freshness mechanism designed in the method further enhance the training efficiency and collaborative effect. The experimental results show that the detection accuracy and efficiency of this paper's method on public datasets are significantly better than the benchmark algorithm.