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AI researchers stranded Claude on a virtual desert island. It started to “play.”1h◆RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods6h◆Can Graph Learning Learn Circuits?6h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery6h◆Trajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification6h◆Sentence-Level Topic Modeling with Lexical and Contextual Evidence6h◆Referring Multi-Object Tracking with Diverse Dynamic Expressions6h◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States6h◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation6h◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model6h◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents6h◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems6h◆Functional Welfare for AI Safety6h◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds6h◆Repairing LLM-Generated Specifications with Deterministic Diagnosis6h◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier6h◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents6h◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation6h◆Repair, Retry, and the Cost of Hindsight: Verifier Feedback in Formal Theorem Proving6h◆Brain-Aligned Multimodal LLMs for Social and Emotional Understanding of Cartoon Movies6h◆AI researchers stranded Claude on a virtual desert island. It started to “play.”1h◆RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods6h◆Can Graph Learning Learn Circuits?6h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery6h◆Trajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification6h◆Sentence-Level Topic Modeling with Lexical and Contextual Evidence6h◆Referring Multi-Object Tracking with Diverse Dynamic Expressions6h◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States6h◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation6h◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model6h◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents6h◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems6h◆Functional Welfare for AI Safety6h◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds6h◆Repairing LLM-Generated Specifications with Deterministic Diagnosis6h◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier6h◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents6h◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation6h◆Repair, Retry, and the Cost of Hindsight: Verifier Feedback in Formal Theorem Proving6h◆Brain-Aligned Multimodal LLMs for Social and Emotional Understanding of Cartoon Movies6h◆
News/Anthropic and Gov. Newsom forge deal allowing California government to use Claude at half price
techcrunch
PublishedJune 29, 2026 at 6:10 PM
—neutral

Anthropic and Gov. Newsom forge deal allowing California government to use Claude at half price

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Publisher summary· verbatim

As Anthropic forges a closer relationship with the state of California, the federal government has made an enemy out of the OpenAI rival.

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