Nearest-neighbor search over embeddings has become a core primitive for AI and LLM-centric workloads. However, prevailing vector databases remain centralized or cluster-bound, introducing single control points, privacy vulnerabilities, and cost/latency bottlenecks. We present Vortex, a decentralized vector overlay that delivers planet-scale approximate nearest neighbor (ANN) search without a centralized control plane. Vortex integrates three key components: (1) Distributed Learned Hashing (DLH), which collaboratively learns piecewise similarity-preserving hash functions to map semantically related vectors to nearby key ranges while balancing load; (2) a Distributed Hash Table (DHT) for scalable, fault-tolerant routing and churn resilience; and (3) a co-designed Distributed HNSW (DHNSW) index for high-recall, low-latency search on each peer. Preliminary results show that Vortex matches the accuracy and latency of leading centralized systems while reducing per-peer index memory requirements by two orders of magnitude and eliminating any central coordinator—enabling fully decentralized, self-organizing ANN overlay for next-generation AI systems.

Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as LLM serving, distributed storage, databases, content delivery networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes a recursive machine learning model to map and retrieve data across a distributed system while preserving the inherent order of data. LEAD includes the designs to minimize range query latency and message cost while maintaining high scalability and resilience to network churn. Our comprehensive evaluations, conducted in both testbed implementation and simulations, demonstrate that LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems, reducing query latency and message cost by 80% to 90%+. Furthermore, LEAD exhibits remarkable scalability and robustness against system churn, providing a robust, scalable solution for efficient data retrieval in distributed key-value systems.

Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as storage systems, decentralized databases, content distribution networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes a recursive machine learning model to map and retrieve data across a distributed system while preserving the inherent order of data. Preliminary results indicate LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems while maintaining high scalability and resilience to network churn.