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Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye
METR analysis finds AI accelerating cyber vulnerability discovery, while SPADE self-play environment generation improves Qwen3 reasoning benchmark scores at 30B scale.
Import AI 470 discusses a METR research note reporting differential acceleration from AI: major acceleration in reported cyber vulnerabilities (cURL, OpenSSL, Firefox, Microsoft, NVD, OSV), minor acceleration in mathematics, and no measurable acceleration in AI-research optimization benchmarks. It also covers SPADE, a self-play framework from a multi-university team (University of Washington, Stanford, MIT, CMU, and others) that co-evolves executable training environments and agent capability using Environment Designer and Reasoning Agent roles with hint-based regret rewards. Trained on Qwen3-4B-Instruct-2507, Qwen3-8B, and Qwen3-30B-A3B-Instruct-2507 via GRPO (400 rollouts of 25 environments), SPADE lifted the 30B-A3B game-environment suite average to 58.3, +8.1 over base, and improved tool-use results across backbones. The issue also references Hawkeye for building better GPU kernels.
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Import AI 468: 23 RSI ideas; PostTrainBench+; and how trust and transparency interplay with AI racing
Import AI covers 23 IFP policy ideas for automated AI R&D risks and MIT/Columbia's game theory of AI racing slowdowns.
Think tank IFP published 23 policy recommendations across seven categories to help policymakers address risks from increasingly automated AI R&D. MIT and Columbia researchers released 'Racing to Ruin,' a game theory model showing that coordinated slowdowns between rival AI firms hinge on trust and transparency. The newsletter also links a short story on interacting with powerful AI systems.
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