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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
Proof of Useful Attestation: A Consensus Primitive for At...
Stefan Stefanović · 2026-05-25 · via cs.DC updates on arXiv.org

Validators on generic Proof of Stake chains earn the same fees whether they handle attestation work correctly or selectively censor it. For chains whose main activity is moving tokens around, that indifference is fine. For chains whose primary economic activity is recording attestations (content provenance, AI-output attribution, threshold-signed credentials, supply-chain receipts), the indifference becomes a problem. Proof of Useful Attestation (PoUA) makes attestation handling first-class in the consensus weighting itself. Validator vote weight is the product of bonded stake and a reputation scalar in [r_min, r_max] that accumulates from valid attestation work. The reputation update is additive, fee-weighted, non-transferable, and capped per epoch. We prove a cost-to-grind floor (Lemma 1): under chain-wide adaptive burn fraction tau_burn, the non-recoverable cost an adversary pays to inflate reputation by Delta_r is bounded below by tau_burn * Delta_r / (eta * alpha_eff). Under the recommended v0 calibration (r_max/r_min in [4, 10]), the cost premium against a capital adversary is 4x to 10x over equivalent pure-stake PoS at steady state. The paper specifies the mechanism, six layered Sybil and grinding defenses, empirical Monte Carlo strategy-search across the full layered defense, and grinding detectors with explicit threshold derivations. It is a mechanism-design proposal with a formal economic floor and inherited BFT safety and liveness, not a complete cryptographic security proof. This release incorporates feedback from Jiangshan Yu (University of Sydney) and Marko Vukolić (Bitcoin Scaling Labs).