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M*: A Modular, Extensible Serving System for Efficient Multimodal AI Inference New Benchmark and Method Address Occlusion in Vision-Language-Action Models for Robotics Fast LLM-Based Semantic Filtering: Unified Framework and Adaptive Two-Phase Method Deliver 1.6–2.0x Speed Gains Google Begins Android 17 Rollout; Key AI Upgrades Coming Later This Year EvalStop: Early Stopping for Reward Overoptimization in Multi-Tenant RLHF Platforms Cordyceps: New Data Poisoning Attack Covertly Controls Large Language Models Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build New Frontier Simulator Cuts LLM Inference Latency Error to Under 3% for Disaggregated Serving US military runs secret ship-to-ship oil transfer operation near Strait of Hormuz to keep Gulf energy exports flowing Wasserstein Equilibrium Decoding Boosts Reliability in Medical Visual Question Answering M*: A Modular, Extensible Serving System for Efficient Multimodal AI Inference New Benchmark and Method Address Occlusion in Vision-Language-Action Models for Robotics Fast LLM-Based Semantic Filtering: Unified Framework and Adaptive Two-Phase Method Deliver 1.6–2.0x Speed Gains Google Begins Android 17 Rollout; Key AI Upgrades Coming Later This Year EvalStop: Early Stopping for Reward Overoptimization in Multi-Tenant RLHF Platforms Cordyceps: New Data Poisoning Attack Covertly Controls Large Language Models Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build New Frontier Simulator Cuts LLM Inference Latency Error to Under 3% for Disaggregated Serving US military runs secret ship-to-ship oil transfer operation near Strait of Hormuz to keep Gulf energy exports flowing Wasserstein Equilibrium Decoding Boosts Reliability in Medical Visual Question Answering
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heterogeneous

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Mosaic: Data-Free Knowledge Distillation Framework Uses Mixture-of-Experts to Tackle Heterogeneous Federated Learning Technology
Artificial Intelligence #mosaic#data-free

Mosaic: Data-Free Knowledge Distillation Framework Uses Mixture-of-Experts to Tackle Heterogeneous Federated Learning

Researchers propose Mosaic, a novel data-free knowledge distillation framework that leverages Mixture-of-Experts (MoE) to overcome model and data heterogeneity in federated learning. Mosaic trains local generative models to synthesize data, forms an MoE from client models, and distills it into a global model. Experiments show consistent outperformance over state-of-the-art approaches on image and multimodal benchmarks.

Jun 16, 2026 1 source
When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning Technology
Artificial Intelligence #federated learning#class-incremental learning

When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning

A new arXiv preprint introduces PRO and PRO-MAX, frameworks that replace synthetic input replay with projected rehearsal orchestration to address degradation in federated class-incremental learning (FCIL) when clients have heterogeneous label subsets and task stages. The methods improve retention and utility across image, text, and graph benchmarks, showing that replay quantity alone does not resolve quality failures.

Jun 16, 2026 1 source