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SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning52m◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning52m◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models52m◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents52m◆Why Muon Outperforms Adam: A Curvature Perspective52m◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies52m◆q0: Primitives for Hyper-Epoch Pretraining52m◆Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach52m◆Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems52m◆SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation52m◆AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation52m◆Widening the Gap: Exploiting LLM Quantization via Outlier Injection52m◆Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction52m◆SaliMory: Orchestrating Cognitive Memory for Conversational Agents52m◆Optimizing Explicit Unit-Distance Lower-Bound Certificates52m◆MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning52m◆Demystifying Multi-Agent Debate: The Role of Confidence and Diversity52m◆Physics-Informed Machine Learning for Short-Term Flood Prediction52m◆Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems52m◆POLARIS: Guiding Small Models to Write Long Stories52m◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning52m◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning52m◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models52m◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents52m◆Why Muon Outperforms Adam: A Curvature Perspective52m◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies52m◆q0: Primitives for Hyper-Epoch Pretraining52m◆Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach52m◆Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems52m◆SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation52m◆AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation52m◆Widening the Gap: Exploiting LLM Quantization via Outlier Injection52m◆Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction52m◆SaliMory: Orchestrating Cognitive Memory for Conversational Agents52m◆Optimizing Explicit Unit-Distance Lower-Bound Certificates52m◆MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning52m◆Demystifying Multi-Agent Debate: The Role of Confidence and Diversity52m◆Physics-Informed Machine Learning for Short-Term Flood Prediction52m◆Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems52m◆POLARIS: Guiding Small Models to Write Long Stories52m◆
News/Osaurus brings both local and cloud AI models to your Mac
techcrunch
PublishedMay 15, 2026 at 12:19 PM
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Osaurus brings both local and cloud AI models to your Mac

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Osaurus combines local and cloud AI models in a Mac app that keeps users’ memory, files, and tools on their own hardware.

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