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Neil Rimer thinks the AI money is coming back out7h◆The Steering Budget: Examples beat Knobs8h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs8h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination8h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models8h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation8h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System8h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets8h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility8h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation8h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks8h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios8h◆When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration8h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents8h◆Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors8h◆Large Audio Language Models for Spoofing-Aware Speaker Verification8h◆ANet Patu-1: The Value of Connection in the Agent Network8h◆Automatically Evolving Prompt Guidelines for Task-Specific Optimization8h◆MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue8h◆Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation8h◆Neil Rimer thinks the AI money is coming back out7h◆The Steering Budget: Examples beat Knobs8h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs8h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination8h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models8h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation8h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System8h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets8h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility8h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation8h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks8h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios8h◆When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration8h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents8h◆Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors8h◆Large Audio Language Models for Spoofing-Aware Speaker Verification8h◆ANet Patu-1: The Value of Connection in the Agent Network8h◆Automatically Evolving Prompt Guidelines for Task-Specific Optimization8h◆MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue8h◆Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation8h◆
News/Anthropic’s Mythos mess is only getting worse
theverge
PublishedJune 26, 2026 at 2:07 PM
—neutral

Anthropic’s Mythos mess is only getting worse

Anthropic’s Mythos mess is only getting worse
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It's been two weeks since Anthropic took its Mythos-class models offline after a Friday evening ultimatum from the Trump administration. The company sprang into action immediately, sending a barrage of executives to Washington, DC. But updates have been suspiciously lacking, with no resolution in si

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