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cs.DC updates on arXiv.org

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
Multi-Round Visibility: A Post-Consensus Ordering Layer f...
Pengkun Ren, Dong Hai, Nasrin Sohrabi, Zahir Tari · 2026-05-22 · via cs.DC updates on arXiv.org

Directed acyclic graph (DAG)-based Byzantine Fault-Tolerant (BFT) protocols achieve high throughput by decoupling dissemination from agreement and allowing many vertices to be committed concurrently. This same concurrency, however, weakens ordering evidence at the execution boundary: once units are committed in a shared DAG frontier, their final linearization is driven by traversal or deterministic tie-breaking rather than verifiable structural precedence. Prior fair-ordering designs address ambiguity by collecting or reconstructing transaction-level ordering evidence within the consensus workflow. While effective, this couples ordering with agreement and places ordering logic on the critical path. This paper presents Multi-Round Visibility (MRV), a post-consensus structural ordering layer for DAG-based BFT that reinterprets the committed DAG as an ordering evidence substrate. Committed vertices inherently carry authenticated creator, round, and ancestry metadata, enabling replicas to derive multi-round structural visibility without extra consensus-path messages. MRV accumulates this visibility within a bounded evidence horizon, compares concurrently committed atomic units of fairness (AUFs) after they coexist in the DAG, and derives precedence constraints from Byzantine-robust visibility advantages. When the DAG lacks such constraints, MRV exposes and resolves the remaining ambiguity through deterministic graph completion rather than hiding it inside traversal rules. We implement MRV on a Narwhal/Tusk-based prototype. Evaluation across 5-50 replicas under various fault settings shows MRV preserves the high-throughput regime of the DAG-BFT stack, proving it provides post-consensus structural ordering without burdening the consensus-critical path.