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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
A historical perspective on developing foundations iInfo(...
Carl Hewitt · 2009-01-31 · via cs.DC updates on arXiv.org

Technology now at hand can integrate all kinds of digital information for individuals, groups, and organizations so their information usefully links together. iInfo(TM) information integration works by making connections including examples like the following: - A statistical connection between "being in a traffic jam" and "driving in downtown Trenton between 5PM and 6PM on a weekday." - A terminological connection between "MSR" and "Microsoft Research." - A causal connection between "joining a group" and "being a member of the group." - A syntactic connection between "a pin dropped" and "a dropped pin." - A biological connection between "a dolphin" and "a mammal". - A demographic connection between "undocumented residents of California" and "7% of the population of California." - A geographical connection between "Leeds" and "England." - A temporal connection between "turning on a computer" and "joining an on-line discussion." By making these connections, iInfo offers tremendous value for individuals, families, groups, and organizations in making more effective use of information technology. In practice, integrated information is invariably pervasively inconsistent. Therefore iInfo must be able to make connections even in the face of inconsistency. The business of iInfo is not to make difficult decisions like deciding the ultimate truth or probability of propositions. Instead it provides means for processing information and carefully recording its provenance including arguments (including arguments about arguments) for and against propositions that is used by iConsult(TM) and iEntertain(TM) apps in iOrgs(TM) Information Systems. A historical perspective on the above questions is highly pertinent to the current quest to develop foundations for privacy-friendly client-cloud computing.