Generative recommendation with large language models (LLMs) reframes prediction as sequence generation, yet existing LLM-based recommenders remain limited in leveraging geographic signals that are crucial in mobility and local-services scenarios.Here, we present REA-SONING OVER SPACE (ROS), a framework that utilizes geography as a vital decision variable within the reasoning process.ROS introduces a Hierarchical Spatial Semantic ID (SID) that discretizes coarse-to-fine locality and POI semantics into compositional tokens, and endows LLM with a three-stage Mobility Chain-of-Thought (CoT) paradigm that models user personality, constructs an intent-aligned candidate space, and performs locality informed pruning.We further align the model with real world geography via spatial-guided Reinforcement Learning (RL).Experiments on three widely used location-based social network (LBSN) datasets show that ROS achieves over 10% relative gains in hit rate over strongest LLM-based baselines and improves cross-city transfer, despite using a smaller backbone model.‡

As critical infrastructure for Web 3.0, DAG-based blockchains promise high throughput for DeFi, IoT, and DApps. However, realizing this potential is challenging, as system performance is dictated by a multitude of interdependent parameters across network, node, and consensus layers. Manual configuration fails to adapt to dynamic workloads, leading to suboptimal performance. We introduce Alzo, a novel auto-tuner that employs hierarchical reinforcement learning (HRL) to navigate this complex configuration space. By decomposing the DAG blockchain's workflow into distinct stages, Alzo's HRL policy learns from stage-level performance metrics to control critical parameters governing consensus, execution, and graph topology in real-time. Furthermore, we employ a shadow-control loop to ensure the safety of all parameter adjustments. Our experiments show that Alzo significantly outperforms other configurations, achieving higher throughput and lower latency under variable workloads with minimal overhead.

The transaction execution layer is a key determinant of throughput in permissioned blockchains. While recent Shared Memory Pools (SMP)-based approaches improve throughput by enabling all consensus nodes to participate in transaction packaging, they face two fundamental limitations. First, the performance bottleneck shifts from the consensus layer to the transaction execution layer as transaction number confirmed in a round increases. Second, these approaches are vulnerable to “transaction duplication” attacks where malicious clients can simultaneously send the same transaction to multiple consensus nodes, thereby decreasing the number of valid transactions in block proposals. To address these limitations, this paper introducesNexus, a novel blockchain transaction processing framework with high scalability.Nexusleverages the idle computational resources of full nodes to enable transaction execution in parallel with the consensus. Moreover,Nexusallows each node to handle only a fraction of the total transactions and share execution results with others. This approach reduces overall transaction execution time, increases throughput, and decreases latency. Lastly,Nexusintroduces a transaction partitioning mechanism that effectively addresses the “transaction duplication” attack and achieves load balancing between clients and consensus nodes. Our implementation ofNexusdemonstrates significant improvements: throughput increases by 4x to 15x, and latency is reduced by 50% to 70%.

Most Bilingual Lexicon Induction (BLI) methods retrieve word translation pairs by finding the closest target word for a given source word based on cross-lingual word embeddings (WEs). However, we find that solely retrieving translation from the source-to-target perspective leads to some false positive translation pairs, which significantly harm the precision of BLI. To address this problem, we propose a novel and effective method to improve translation pair retrieval in cross-lingual WEs. Specifically, we consider both source-side and target-side perspectives throughout the retrieval process to alleviate false positive word pairings that emanate from a single perspective. On a benchmark dataset of BLI, our proposed method achieves competitive performance compared to existing state-of-the-art (SOTA) methods. It demonstrates effectiveness and robustness across six experimental languages, including similar language pairs and distant language pairs, under both supervised and unsupervised settings.

Blockchain sharding technology has been used to enhance the scalability of blockchain systems. As the number of shards increases, the high latency inherent in cross-shard transactions gradually becomes a bottleneck, hindering improvements in overall system efficiency. Therefore, reducing the latency of cross-shard transactions is significantly important. However, existing mechanisms for handling cross-shard transactions fail to minimize the latency of cross-shard transactions and have not fully used the bandwidth available within shards. In this paper, we introduce Presto, a protocol designed for the account-state-based blockchain, which reduces the latency of handling cross-shard transactions. Presto leverages the concept of optimistic pre-execution along with pending tree to optimize cross-shard transaction processing. Presto also employs predistribution of cross-shard transactions with Erasure Coding to efficiently utilize bandwidth resources. We have developed an prototype and conducted extensive experiments on a cloud platform. The evaluation results indicate that Presto surpasses existing solutions in terms of system throughput, transaction confirmation latency, and mempool queue size, demonstrating Presto's potential to significantly improve blockchain scalability and user experience.

Sharding provides an opportunity to overcome the inherent scalability challenges of the blockchain, which is the infrastructure for the next generation of the Web. In a sharding blockchain, the state is partitioned into smaller groups known as "shards." Since the states are placed on different shards, cross-shard transactions are inevitable, which is detrimental to the performance of the sharding blockchain. Existing solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less effective or costly. In this paper, we present SPRING, the first deep-reinforcement-learning(DRL)-based sharding framework for state placement. SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy. Experimental results based on real Ethereum transaction data demonstrate the superiority of SPRING compared to other state placement solutions. In particular, it decreases the cross-shard transaction ratio by up to 26.63% and boosts throughput by up to 36.03%, all without unduly sacrificing the workload balance among shards. Moreover, updating the training model and making decisions takes only 0.1s and 0.002s, respectively, which shows the overhead is acceptable.

In permissioned blockchains, the bandwidth of consensus nodes is mainly consumed by transaction ordering and block distribution; hence, the allocation of consensus nodes' bandwidth makes a significant difference to the system throughput. Previous research focuses on the consensus layer and attempts to optimize consensus protocols to improve throughput, which, however, neglects the impact of data distribution on the throughput and transfers performance bottlenecks to the network layer. In fact, the overall throughput of permissioned blockchains is co-determined by data production in the consensus layer and data distribution in the network layer. This paper proposes a novel data flow framework composed of Predis and Multi-Zone. The former is a data production strategy for permissioned blockchains that employ leader-based BFT protocols and the latter, its corresponding network topology. Predis enables each consensus node to contribute its idle bandwidth for block content pre-distribution so that a much higher volume of transactions can be confirmed in one consensus round, significantly increasing consensus efficiency. Multi-Zone is a network topology to distribute blocks. It can regulate the bandwidth consumption of consensus nodes at a certain value during data distribution and effectively reduce block propagation latency. To test our framework, we implement Predis based on Hotstuff and PBFT, respectively, and experiments show that Predis significantly improves their throughput by 300% to 800%. Multi-Zone is implemented on BFT-SMaRt and compared with random and star network topologies, and it is shown that Multi-Zone holds excellent scalability and the capability of reducing block propagation latency by at least 50%.

The analysis of user behavior provides a large amount of useful information. After being extracted, this information is called user knowledge. User knowledge plays a guiding role in implementing user-centric updates for software platforms. A good representation and application of user knowledge can accelerate the development of a software platform and improve its quality. This paper aims to further the utilization of user knowledge by mining the user knowledge that is implicit in user behavior and then constructing a knowledge graph of this behavior. First, the association between a software bug and a software component is mined from the user knowledge. Then, the knowledge entity extraction and relationship extraction are performed from the development code and the user behavior. Finally, the knowledge is stored in the graph database, from which it can be visually retrieved. Relevant experiments on CIFLog, an integrated logging processing software platform, have proved the effectiveness of this research. Constructing a user behavior knowledge graph can improve the utilization of user knowledge as well as the quality of software platform development.