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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
did:crdt: Coordination-Free Decentralised Identifiers via...
Hugo O'Connor, Claire Barnes · 2026-06-15 · via cs.DC updates on arXiv.org

Existing Decentralised Identifier (DID) methods require coordination, an agreed global order of operations, to update a DID document: blockchain-anchored methods incur fees and latency; lightweight peer methods (did:key, did:peer) offer no update mechanism; and Sidetree methods still require blockchain ordering for finality. We present did:crdt, a DID method that targets W3C DID Core and removes the need for coordination entirely: there is no ledger, no sequencer, and no global total order. Each DID document is composed of signed Conflict-Free Replicated Data Types (CRDTs), one per document field, each chosen so that concurrent edits merge deterministically. By the CALM Theorem, the state-merge path is then confluent: replicas that see the same updates reach the same document in any arrival order. The signed-delta path needs only causal delivery, applying an update after those it builds on, which is far weaker than the total ordering ledgers impose and needs no agreement protocol. We are explicit about scope: every untrusted-peer path is authenticated, so Byzantine fault tolerance (safety even when peers lie or send malformed data) holds for signed deltas and verified-bundle replay, while the unauthenticated state-merge path is a trusted-domain optimisation and key-compromise recovery is bounded by revocation semantics. We give the data and threat model, CRUD semantics, conflict resolution, and a Rust reference implementation with property-based convergence tests and microsecond-scale merge latency.