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[AINews] LLMs can read & write neural network weights: "Weight Space Programming" cuts Autoresearch out of the RSI loop
Research

[AINews] LLMs can read & write neural network weights: "Weight Space Programming" cuts Autoresearch out of the RSI loop

Recent AI developments feature editing Qwen's weights to reduce reasoning loops, a DeepSeek super-agent prototype, and the GDPx benchmark.

ResearchAgents & automation
#DeepSeek#llms#neural networks#benchmarks#agents#AI Agents

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Observed across 2 sources

1 editorial report · 1 verified social mention. The most authoritative report leads while later evidence completes the story.

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[AINews] LLMs can read & write neural network weights: "Weight Space Programming" cuts Autoresearch out of the RSI loop

Open report

Social corroboration

reddit9h before report

Link: https://rikiyat.github.io/blog/weight-space-programming/ Snippet: “We find that frontier LLMs are surprisingly good at natively reading and writing neural network weights , well past what we’d expect from humans or conventional algorithms. This might be a big deal for both optimization and interpretability. Just by looking at the raw weights, GPT-6 Astra can tell what a network was trained to do . And without any environment feedback, it has no trouble writing, from scratch, small game-playing networks that outplay networks trained with PPO for 100 million steps.” Very surprising result, I didn’t expect frontier models to have this capability that has not been optimized for, and it’s something humans cannot do either. submitted by /u/RobbinDeBank [link] [comments]

Open mention
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