--- format: "aidr-story-markdown/v1" id: "15b07f7e71af672e7befaa6c4e48d56fef51537b897f47e77ed129587f7714e3" canonical_url: "https://aidr.today/15b07f7e?lang=en" title: "LLMs are not superhuman at reading and writing neural network weights" lang: "en" requested_lang: "en" available_langs: ["en"] translation_fallback: null fallback_fields: [] published_at: "2026-11-02T22:26:53.000Z" category: "Research" topics: ["openai","llm"] source_urls: ["https://www.seangoedecke.com/llms-are-not-superhuman-at-reading-and-writing-neural-network-weights/","https://news.ycombinator.com/item?id=50344861"] summary: "A recent paper argues that frontier LLMs can work with neural network weights directly. This seems superhuman, but it isn’t. The paper shows that GPT-6 Astra can take a small neural network and interact with its weights directly, “well past what we’d expect from humans or conventional algorithms”. Specifically, Astra was able to identify what games a small network 1 was trained to play just by looking at the weights, and even tweak the weights itself. The most impressive result is Astra significantly improving a 0.8B Qwen LLM’s ability on mathematics by natively editing its weights. The reason to be excited here is that “superhuman ability to improve language models” is precisely the kind of thing that could cause recursive self-improvement. I think a smart human ML researcher could perform all of these tasks . Figuring out what game a model was trained to play based on the network sounds impressive until you consider three points: Here’s that calculation expressed as a heatmap in three lines of Python: If someone showed you that heatmap without the game labels, I think you’d be able to immediately identify at least the first three games purely by noticing where the player is (e.g…" --- # LLMs are not superhuman at reading and writing neural network weights > [Open the canonical story]() **Published:** 2026-11-02T22:26:53.000Z **Category:** Research **Topics:** openai, llm ## Summary A recent paper argues that frontier LLMs can work with neural network weights directly\. This seems superhuman, but it isn’t\. The paper shows that GPT\-6 Astra can take a small neural network and interact with its weights directly, “well past what we’d expect from humans or conventional algorithms”\. Specifically, Astra was able to identify what games a small network 1 was trained to play just by looking at the weights, and even tweak the weights itself\. The most impressive result is Astra significantly improving a 0\.8B Qwen LLM’s ability on mathematics by natively editing its weights\. The reason to be excited here is that “superhuman ability to improve language models” is precisely the kind of thing that could cause recursive self\-improvement\. I think a smart human ML researcher could perform all of these tasks \. Figuring out what game a model was trained to play based on the network sounds impressive until you consider three points: Here’s that calculation expressed as a heatmap in three lines of Python: If someone showed you that heatmap without the game labels, I think you’d be able to immediately identify at least the first three games purely by noticing where the player is \(e\.g… ## Sources - [Story source]() - [Discussion]()