Low-Rank Adaptation (LoRA) has become increasingly popular for efficiently fine-tuning large language models (LLMs) with minimal resources. However, traditional methods that serve multiple LoRA models independently result in redundant computation and low GPU utilization. This paper addresses these inefficiencies by introducing Dynamic Operator Optimization (Dop), an advanced automated optimization technique designed to dynamically optimize the Segmented Gather Matrix-Vector Multiplication (SGMV) operator based on specific scenarios. SGMV's unique design enables batching GPU operations for different LoRA models, significantly improving computational efficiency. The Dop approach leverages a Search Space Constructor to create a hierarchical search space, dividing the program space into high-level structural sketches and low-level implementation details, ensuring diversity and flexibility in operator implementation. Furthermore, an Optimization Engine refines these implementations using evolutionary search, guided by a cost model that estimates program performance. This iterative optimization process ensures that SGMV implementations can dynamically adapt to different scenarios to maintain high performance. We demonstrate that Dop can improve throughput by 1.30-1.46 times in a SOTA multi-tenant LoRA serving.

Drug discovery is crucial for identifying candidate drugs for various diseases. However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem. Existing methods primarily focus on single-scale features, overlooking the hierarchical molecular structures that determine different molecular properties. To address these issues, we introduce Universal Matching Networks (UniMatch), a dual matching framework that integrates explicit hierarchical molecular matching with implicit task-level matching via meta- learning, bridging multi-level molecular representations and task-level generalization. Specifically, our approach explicitly captures structural features across multiple levels—atoms, substructures, and molecules—via hierarchical pooling and matching, facilitating precise molecular representation and comparison. Additionally, we employ a meta-learning strategy for implicit task-level matching, allowing the model to capture shared patterns across tasks and quickly adapt to new ones. This unified matching framework ensures effective molecular alignment while leveraging shared meta-knowledge for fast adaptation. Our experimental results demonstrate that UniMatch outperforms state-of-the-art methods on the MoleculeNet and FS-Mol benchmarks, achieving improvements of 2.87% in AUROC and 6.52% in ∆AUPRC. UniMatch also shows excellent generalization ability on the Meta-MolNet benchmark.

Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning method for large language models (LLMs) to adapt to downstream tasks. However, in scenarios where multiple LoRA models are deployed simultaneously, standard LoRA introduces substantial trainable parameters, resulting in significant memory overhead and inference latency, particularly when supporting thousands of downstream tasks on a single server. While existing methods reduce stored parameters via parameter sharing, they fail to capture both local and global information simultaneously. To address this issue, we propose the Bi-Share LoRA (BSLoRA), which extends local LoRA with intra-LoRA and inter-LoRA parameter sharing to better capture local and global information. This approach reduces trainable parameters while maintaining or even enhancing model performance. Additionally, we design three transformation methods to improve the compatibility and collaborative efficiency of shared parameters with varying shapes, enhancing overall adaptability. Experiments on the 7B, 8B, and 13B versions of Llama show that BSLoRA, with only 44.59% of the parameters of standard LoRA, outperforms LoRA by approximately 0.33% on commonsense reasoning and 2.08% on MMLU benchmarks. Code is available at https://github.com/yuhua-zhou/BSLoRA.git.

The efficiency of warehouses is vital to e-commerce. Fast order processing at the warehouses ensures timely deliveries and improves customer satisfaction. However, monitoring, analyzing, and manipulating order processing in the warehouses in real time are challenging for traditional methods due to the sheer volume of incoming orders, the fuzzy definition of delayed order patterns, and the complex decision-making of order handling priorities. In this paper, we adopt a data-driven approach and propose OrderMonitor, a visual analytics system that assists warehouse managers in analyzing and improving order processing efficiency in real time based on streaming warehouse event data. Specifically, the order processing pipeline is visualized with a novel pipeline design based on the sedimentation metaphor to facilitate real-time order monitoring and suggest potentially abnormal orders. We also design a novel visualization that depicts order timelines based on the Gantt charts and Marey's graphs. Such a visualization helps the managers gain insights into the performance of order processing and find major blockers for delayed orders. Furthermore, an evaluating view is provided to assist users in inspecting order details and assigning priorities to improve the processing performance. The effectiveness of OrderMonitor is evaluated with two case studies on a real-world warehouse dataset.