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Agentic Performance at the Edge: Insights from Benchmarking Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference PAAC: Privacy-Aware Agentic Device-Cloud Collaboration Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration Private Vertical Federated Inference for Time-Series Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning \mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment SparseRL-Sync: Lossless Weight Synchronization with ~100x Less Communication Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning Execution Envelopes: A Shared Admission Contract for Backend AI Execution Requests Regulating Branch Parallelism in LLM Serving CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification CCL-Bench 1.0: A Trace-Based Benchmark for LLM Infrastructure Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence VibeServe: Can AI Agents Build Bespoke LLM Serving Systems? 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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
Advancements in Robotics Process Automation: A Novel Model with Enhanced Empirical Validation and Theoretical Insights
Gokul Pandy, Vivekananda Jayaram, Manjunatha Sughaturu Krishnapp · 2024-10-06 · via cs.DC updates on arXiv.org

Robotics Process Automation is revolutionizing business operations by significantly enhancing efficiency, productivity, and operational excellence across various industries. This manuscript delivers a comprehensive review of recent advancements in RPA technologies and proposes a novel model designed to elevate RPA capabilities.

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