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SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning44m◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning44m◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models44m◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents44m◆Why Muon Outperforms Adam: A Curvature Perspective44m◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies44m◆q0: Primitives for Hyper-Epoch Pretraining44m◆Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach44m◆Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems44m◆SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation44m◆AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation44m◆Widening the Gap: Exploiting LLM Quantization via Outlier Injection44m◆Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction44m◆SaliMory: Orchestrating Cognitive Memory for Conversational Agents44m◆Optimizing Explicit Unit-Distance Lower-Bound Certificates44m◆MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning44m◆Demystifying Multi-Agent Debate: The Role of Confidence and Diversity44m◆Physics-Informed Machine Learning for Short-Term Flood Prediction44m◆Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems44m◆POLARIS: Guiding Small Models to Write Long Stories44m◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning44m◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning44m◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models44m◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents44m◆Why Muon Outperforms Adam: A Curvature Perspective44m◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies44m◆q0: Primitives for Hyper-Epoch Pretraining44m◆Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach44m◆Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems44m◆SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation44m◆AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation44m◆Widening the Gap: Exploiting LLM Quantization via Outlier Injection44m◆Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction44m◆SaliMory: Orchestrating Cognitive Memory for Conversational Agents44m◆Optimizing Explicit Unit-Distance Lower-Bound Certificates44m◆MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning44m◆Demystifying Multi-Agent Debate: The Role of Confidence and Diversity44m◆Physics-Informed Machine Learning for Short-Term Flood Prediction44m◆Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems44m◆POLARIS: Guiding Small Models to Write Long Stories44m◆
News/Spotify adds AI-powered Q&A and briefing generation features to podcasts
techcrunch
PublishedMay 21, 2026 at 3:27 PM
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Spotify adds AI-powered Q&A and briefing generation features to podcasts

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Spotify will let you generate daily or weekly briefs based on your prompts

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A 5-min weekly brief — top movers, price watch, story of the week.

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