Transaction execution remains a major performance bottleneck in blockchains. Existing parallel execution engines are still blocked by slow state accesses on each transaction's critical path, creating a hidden loss of parallelism. This paper presents Weaver, a state-decoupled transaction processing paradigm that enables execution flow to proceed in the absence of concrete state values. Weaver represents states as lightweight symbolic references and records subsequent computation as symbolic expressions while concrete values are fetched on the fly. To handle state-dependent control flow, Weaver predicts the most likely branch direction through hierarchical symbolic pattern matching. Weaver further introduces expression-granular scheduling that evaluates each symbolic expression as soon as its state dependencies are resolved, rather than waiting for the transaction's entire read set, fully exploiting fine-grained intra-transaction parallelism. Evaluation on real Ethereum blocks and synthetic workloads spanning four representative dApps shows that Weaver delivers 41%-63% higher throughput than state-of-the-art parallel execution engines and up to 7.5 times the throughput of serial execution.
Visual foundation models learn rich pathological morphology but lack explicit concept-level supervision. Vision--language pretraining can provide this supervision but requires costly image--text pairs. Here, we introduce GloSAGE, a semantic self-distillation framework that uses lesion profiles predicted by a pretrained visual teacher to construct structured pathology descriptions. Reliability-guided selection identifies image--text anchors, while Knowledge-Structured Pathology Text Synthesis and Evidence-Guided Negative-pair Construction account for partially observed lesions. Image--text alignment trains projection layers while retaining the frozen visual encoder; an intermediate Semantic Transfer Layer supplies features for downstream tasks. Developed on GloMillion and evaluated across seven glomerulus- and case-level cohorts, GloSAGE supported zero-shot lesion recognition, image--text retrieval, hard-negative discrimination and concept-intervention analyses. It outperformed its source visual model in most lesion-recognition tasks and improved lesion grading, while supporting competitive case-level disease classification, injury grading and baseline renal function estimation. Ablations supported the combined contributions of knowledge-structured text and evidence-guided negatives, and the intermediate transfer layer improved mean lesion-recognition ROC-AUC relative to the complete projection output. A frozen language model conditioned on GloSAGE features generated glomerular descriptions. These findings suggest that teacher-derived lesion profiles can provide annotation-efficient semantic supervision for extending a glomerular visual foundation model.