Guangda Sun

Kaiwen Tang, Jiaqi Zheng, Yuze Jin et al.

Time-series forecasting in domains like traffic management and industrial monitoring often requires real-time, energy-efficient processing on edge devices with limited resources. Spiking neural networks (SNNs) offer event-driven computation and ultra-low power and have been proposed for use in this space. Unfortunately, existing SNN-based time-series forecasters often use complex transformer blocks. To address this issue, we propose SpikySpace, a spiking state-space model (SSM) that reduces the quadratic cost in the attention block to linear time via spiking selective scanning. Further, we introduce PTsoftplus and PTSiLU, two efficient approximations of SiLU and Softplus that replace costly exponential and division operations with simple bit-shifts. Evaluated on four multivariate time-series benchmarks, SpikySpace outperforms the leading SNN in terms of accuracy by up to 3.0% while reducing energy consumption by over 96.1%. As the first fully spiking state-space model, SpikySpace bridges neuromorphic efficiency with modern sequence modeling, opening a practical path toward efficient time series forecasting systems. Our code is available at https://anonymous.4open.science/r/SpikySpace.

Guangda Sun, Jialin Li

Decentralized storage networks (DSNs) are storage systems powered by permissionless nodes. Data placement in DSNs must tolerate not only storage-device failures but also adversarial behavior that targets data availability. Byzantine nodes introduce unique challenges due to collusion and adaptive attacks. They can target specific data blocks by clustering within a block's placement group, reducing the number of rational nodes and weakening failure tolerance. In this work, we propose a global defense against Byzantine nodes across all placement groups. We introduce a node-centric approach that guarantees stable incentives for rational nodes regardless of the number of Byzantine nodes in their placement groups. Building on this approach, we design Vault, a DSN that uses sampling-based data placement with verifiable randomness. Compared with prior DSNs, this placement strategy allows Vault to scale simultaneously in storage volume, on-chain footprint, and Byzantine tolerance. Our preliminary results show that Vault achieves the desired availability with scalable storage overhead while maintaining scalable fault tolerance.

Guangda Sun, Jialin Li

Storage scalability is paramount in the era of big data blockchain. A storage-scalable blockchain can effectively scale out state storage to an arbitrary number of nodes and reduce the storage pressure on each, similar to distributed databases. Prior research has extensively utilized sharding techniques to attain storage scalability; however, these approaches invariably compromise safety and liveness guarantees. In this work, we propose a novel state-execution decoupled architecture, and Supermassive Blockchain, a novel storage-scalable Byzantine fault tolerance (BFT) protocol that can sustain the deterministic security properties of conventional BFT protocols. The state management system employs erasure coding to ensure state availability with scalable storage consumption, while the global consensus and execution layers maintain robust security characteristics. Our evaluation indicates that Supermassive Blockchain achieves better storage scalability compared to prior approaches while incurring low network overhead.

Kaiwen Tang, Aitong Wu, Yao Lu et al.

Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues combined with user ratings and attribution analysis to pinpoint high-value knowledge fragments, and injects these fragments via in-context prompts for lightweight domain adaptation. With high-value knowledge, the LLM can generate more accurate and domain-specific content. In a financial information scenario, we collect 15k feedback from about 120 users and validate CoEM with user ratings to assess the quality of generated insights, demonstrating significant improvements in domain-specific generation while avoiding the time and compute overhead of traditional fine-tuning workflows.