Optimizing the advertiser's cumulative value of winning impressions under budget constraints poses a complex challenge in online advertising, under the paradigm of AI-Generated Bidding (AIGB). Advertisers often have personalized objectives but limited historical interaction data, resulting in few-shot scenarios where traditional reinforcement learning (RL) methods struggle to perform effectively. Large Language Models (LLMs) offer a promising alternative for AIGB by leveraging their in-context learning capabilities to generalize from limited data. However, they lack the numerical precision required for fine-grained optimization. To address this limitation, we introduce GRPO-Adaptive, an efficient LLM post-training strategy that enhances both reasoning and numerical precision by dynamically updating the reference policy during training. Built upon this foundation, we further propose DARA, a novel dual-phase framework that decomposes the decision-making process into two stages: a few-shot reasoner that generates initial plans via in-context prompting, and a fine-grained optimizer that refines these plans using feedback-driven reasoning. This separation allows DARA to combine LLMs' in-context learning strengths with precise adaptability required by AIGB tasks. Extensive experiments on both real-world and synthetic data environments demonstrate that our approach consistently outperforms existing baselines in terms of cumulative advertiser value under budget constraints.

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%.

Sharding blockchain networks face significant scalability challenges due to high frequencies of cross-shard transactions and uneven workload distributions among shards. To address these scalability issues, account migration offers a promising solution. However, existing migration solutions struggle with the high computational overhead and insufficient capture of complex transaction patterns. We propose AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains. AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle. Extensive evaluation with real Ethereum transaction data demonstrates that AERO improves the system throughput by 31.77% compared to existing solutions, effectively reducing cross-shard transactions and balancing shard workloads.

Adversarial distillation (AD) is a highly effective method for enhancing the robustness of small models. Contrary to expectations, a high-performing teacher model does not always result in a more robust student model. This is due to two main reasons. First, when there are significant differences in predictions between the teacher model and the student model, exact matching of predicted values using KL divergence interferes with training, leading to poor performance of existing methods. Second, matching solely based on the output prevents the student model from fully understanding the behavior of the teacher model. To address these challenges, this paper proposes a novel AD method named SmaraAD. During the training process, we facilitate the student model in better understanding the teacher model's behavior by aligning the attribution region that the student model focuses on with that of the teacher model. Concurrently, we relax the condition of exact matching in KL divergence and replace it with a more flexible matching criterion, thereby enhancing the model's robustness. Extensive experiments substantiate the effectiveness of our method in improving the robustness of small models, out-performing previous SOTA methods.

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.

Abstract In recent years, network coding has received extensive attention and has been applied to various computer network systems, since it has been mathematically proven to enhance the network robustness and maximize the network throughput. However, it is well-known that network coding is extremely vulnerable to pollution attacks. Certificateless network coding scheme (CLNS) is a recently proposed mechanism to defend against pollution attacks for network coding, which avoids tedious management of certificates and key-escrow attack. Until now, only a few constructions were presented, and more ones should be given so as to enrich this field. In this paper, for the first time, we study the general construction of CLNS from certificateless public auditing protocol (CL-PAP), although the two areas seem to be quite different in their nature and are studied independently. Since there are many candidates of CL-PAPs, we can naturally obtain abundant constructions of CLNSs according to our systematic way. In addition, in order to show the power of the general construction, we also present a concrete implementation given a specific CL-PAP. The performance analysis and experimental results show that the implemented CLNS is competitive in the existing network coding schemes.