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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
MEV-ACE: Identity-Authenticated Fair Ordering for Propose...
Jian Sheng Wang · 2026-04-09 · via cs.DC updates on arXiv.org

Maximal Extractable Value, or MEV, remains a structural threat to blockchain fairness because a block producer can often observe pending transactions and unilaterally decide their ordering or inclusion. Existing mitigations hide transaction contents or outsource ordering, but they often leave two gaps unresolved. First, commitments are not authenticated by slashable identities. Second, inclusion obligations are not backed by transferable evidence that other validators can verify. This paper presents MEV ACE, a fair ordering protocol for proposer controlled ordering MEV. MEV ACE combines three mechanisms. First, it uses registered economic identities whose authentication keys are deterministically derived from the ACE GF framework and bonded on chain. Second, it uses authenticated commit and open messages with validator receipt thresholds, which make admissibility and inclusion obligations independently auditable. Third, it uses verifiable delay based randomness to determine transaction order only after the admissible commitment set is fixed. We formalize the protocol in a Byzantine fault tolerant validator model with threshold receipts and show three properties under standard assumptions: order unpredictability after the admissible set is locked, commitment authenticity under signature unforgeability, and accountable inclusion for transactions that obtain threshold commit and open receipts. Under these conditions, and when producer and user bonds exceed the one slot gain from invalid execution or selective non opening, MEV ACE removes unilateral proposer discretion over front running, sandwich attacks, and censorship against admitted transactions. The protocol remains single slot in structure, requires no threshold decryption committee, and is compatible with post quantum signature schemes such as ML DSA 44.