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SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning47m◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning47m◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models47m◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents47m◆Why Muon Outperforms Adam: A Curvature Perspective47m◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies47m◆q0: Primitives for Hyper-Epoch Pretraining47m◆Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach47m◆Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems47m◆SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation47m◆AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation47m◆Widening the Gap: Exploiting LLM Quantization via Outlier Injection47m◆Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction47m◆SaliMory: Orchestrating Cognitive Memory for Conversational Agents47m◆Optimizing Explicit Unit-Distance Lower-Bound Certificates47m◆MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning47m◆Demystifying Multi-Agent Debate: The Role of Confidence and Diversity47m◆Physics-Informed Machine Learning for Short-Term Flood Prediction47m◆Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems47m◆POLARIS: Guiding Small Models to Write Long Stories47m◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning47m◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning47m◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models47m◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents47m◆Why Muon Outperforms Adam: A Curvature Perspective47m◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies47m◆q0: Primitives for Hyper-Epoch Pretraining47m◆Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach47m◆Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems47m◆SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation47m◆AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation47m◆Widening the Gap: Exploiting LLM Quantization via Outlier Injection47m◆Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction47m◆SaliMory: Orchestrating Cognitive Memory for Conversational Agents47m◆Optimizing Explicit Unit-Distance Lower-Bound Certificates47m◆MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning47m◆Demystifying Multi-Agent Debate: The Role of Confidence and Diversity47m◆Physics-Informed Machine Learning for Short-Term Flood Prediction47m◆Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems47m◆POLARIS: Guiding Small Models to Write Long Stories47m◆
News/Robinhood will let your AI agent trade stocks and make (or lose) lots of money
theverge
PublishedMay 27, 2026 at 2:36 PM
—neutral

Robinhood will let your AI agent trade stocks and make (or lose) lots of money

Robinhood will let your AI agent trade stocks and make (or lose) lots of money
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Publisher summary· verbatim

Robinhood is opening its trading platform to AI agents. In an announcement on Wednesday, Robinhood says traders can now create a separate account for an AI agent and add a specific amount of money, allowing the agent to buy and sell stocks across the market. The company pitches the feature as a way

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