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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
NVLang: Unified Static Typing for Actor-Based Concurrency...
2025-12-05 · via cs.DC updates on arXiv.org

Actor-based systems like Erlang/OTP power critical infrastructure -- from telecommunications to messaging platforms -- handling millions of concurrent connections with legendary reliability. Yet these systems lack static guarantees about message protocols: processes communicate by sending arbitrary messages that pattern-matched at runtime, deferring protocol violations to production failures. We present NVLang, a statically typed functional language that brings comprehensive type safety to the BEAM virtual machine while preserving actor model's simplicity and power. NVLang's central contribution that algebraic data types (ADTs) naturally encode actor message protocols: each actor declares the sum type representing its message vocabulary, and the type system enforces protocol conformance at compile time. We introduce typed process identifiers (Pid[T]) that encode the protocol an actor expects, and typed futures (Future[T]) that provide type-safe request-reply patterns. By extending Hindley-Milner type inference to track message protocols, NVLang eliminates an entire class of message-passing errors while maintaining clean syntax that rivals dynamically typed alternatives. Our implementation compiles to Core Erlang, enabling seamless interoperability with the existing Erlang ecosystem. We formalize the type system and provide proof sketches for type soundness, demonstrating that well-typed NVLang programs cannot send messages that violate actor protocols.