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How Endava is redesigning software delivery around AI agents-157m◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning5h◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning5h◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models5h◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents5h◆Why Muon Outperforms Adam: A Curvature Perspective5h◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies5h◆Efficient Reasoning on the Edge5h◆MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models5h◆Inference-Time Vulnerability Beyond Shallow Safety: Alignment Along Generation Trajectories5h◆Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems5h◆Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach5h◆Belief-Aware VLM Model for Human-like Reasoning5h◆Binary Spiking Neural Networks as Causal Models5h◆SciIntegrity-Bench: A Benchmark for Evaluating Academic Integrity in AI Scientist Systems5h◆Bad Seeing or Bad Thinking? Rewarding Perception for Multimodal Reasoning5h◆Unlocking Proactivity in Task-Oriented Dialogue5h◆The Illusion of Opting in AI-Mediated Consequential Decisions5h◆SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition5h◆Subliminal Learning Is Steering Vector Distillation5h◆How Endava is redesigning software delivery around AI agents-157m◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning5h◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning5h◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models5h◆Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents5h◆Why Muon Outperforms Adam: A Curvature Perspective5h◆Provably Auditable and Safe LLM Agents from Human-Authored Ontologies5h◆Efficient Reasoning on the Edge5h◆MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models5h◆Inference-Time Vulnerability Beyond Shallow Safety: Alignment Along Generation Trajectories5h◆Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems5h◆Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach5h◆Belief-Aware VLM Model for Human-like Reasoning5h◆Binary Spiking Neural Networks as Causal Models5h◆SciIntegrity-Bench: A Benchmark for Evaluating Academic Integrity in AI Scientist Systems5h◆Bad Seeing or Bad Thinking? Rewarding Perception for Multimodal Reasoning5h◆Unlocking Proactivity in Task-Oriented Dialogue5h◆The Illusion of Opting in AI-Mediated Consequential Decisions5h◆SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition5h◆Subliminal Learning Is Steering Vector Distillation5h◆
News/Closing time
theverge
PublishedMay 14, 2026 at 10:21 PM
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Closing time

Closing time
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Today was closing arguments in the Musk v. Altman trial, and I almost feel bad writing about the unbelievable demolition derby I just witnessed. Steven Molo, Musk's lawyer, stumbled over his words. He at one point called Greg Brockman - a co-defendant - Greg Altman. He erroneously claimed that Musk

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