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An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response38m◆Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training38m◆Large language model and reinforcement learning based autonomous construction and progress updates38m◆Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models38m◆MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring38m◆DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution38m◆Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing38m◆Fix, Not Just Resample: Bounded Compiler-Guided Repair in Lean 438m◆MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning38m◆LightCRS: Lightweight Multi-Domain Conversational Recommendation System38m◆When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability38m◆SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents38m◆Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?38m◆Teaching AI Agent to Play Poker Strategically Through Preference Optimization38m◆M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement38m◆Physical AI: Concepts, Techniques, and Challenges38m◆ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems38m◆A general-purpose System One decision model for clinical records38m◆Omni-Woodpecker: Hallucination Correction via Evidence-Prior Rebalancing for Omni LLMs38m◆PROVISO: Provenance-Informed Action Selection for Secure LLM Agents38m◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response38m◆Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training38m◆Large language model and reinforcement learning based autonomous construction and progress updates38m◆Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models38m◆MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring38m◆DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution38m◆Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing38m◆Fix, Not Just Resample: Bounded Compiler-Guided Repair in Lean 438m◆MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning38m◆LightCRS: Lightweight Multi-Domain Conversational Recommendation System38m◆When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability38m◆SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents38m◆Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?38m◆Teaching AI Agent to Play Poker Strategically Through Preference Optimization38m◆M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement38m◆Physical AI: Concepts, Techniques, and Challenges38m◆ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems38m◆A general-purpose System One decision model for clinical records38m◆Omni-Woodpecker: Hallucination Correction via Evidence-Prior Rebalancing for Omni LLMs38m◆PROVISO: Provenance-Informed Action Selection for Secure LLM Agents38m◆
News/Anthropic co-founder confirms the company briefed the Trump administration on Mythos
techcrunch
PublishedApril 14, 2026 at 6:09 PM
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Anthropic co-founder confirms the company briefed the Trump administration on Mythos

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In an interview at the Semafor World Economy summit this week, Anthropic co-founder Jack Clark explained why the company was still engaged with the U.S. government while simultaneously suing them.

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