Post
92
All 1,125 turns in eight weight-space experiments with OpenAI's gpt-6-astra are logged and audited as tool-free, and the repo already has 877 stars and 109 forks.
The logs support "weights are an interface worth probing". They do not support "LLMs natively program neural networks". Every prompt, reply, code run and evaluation is published, so you can check where the line falls. The paper has 12,564 alphaXiv visits and 511 votes this week.
🔍 What was tested
• Reading: which game a network was trained on (Exp 1), what a diffusion model generates (Exp 2), which language an LM learned (Exp 3)
• Editing: fix a failing network (Exp 4), improve Qwen3.5-0.8B at math with 10, 25 and 50 passes (Exp 8)
• Writing: MinAtar game networks (Exp 5), a 1.5M-weight Go network (Exp 6)
⚠️ Caveats
• Exps 1, 2, 4, 5 had no tools. Exps 3, 6, 7, 8 used a no-network bubblewrap sandbox, one Python block per turn
• Effort differs: high (1, 2, 4), xhigh (3, 5, 6, 7), max (8), so results are not like-for-like
• One closed model, the authors' own runs, and trial runs of earlier prompts are left out
🛠️ Audit it yourself
• Check audit.tool_free in each turn's run.json and the raw events.jsonl
• Open exp8_math: prompts, tool_calls.jsonl, eval files. Edits ship as exact diffs against the public base weights, with an apply script
https://github.com/RikiyaT/lm_understand-logs
https://huggingface.co/papers/2611.weight-space-programming
Which experiment would you rerun first with an open-weight model?
📚 Read it elsewhere
• The deep dive on Medium: https://medium.com/p/3ed2af66a0c9
• The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/openais-gpt-6-astra-reads-network
• The thread on X: https://x.com/saavoji/status/2118036030452650268
• Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#openais-gpt-6-10
The logs support "weights are an interface worth probing". They do not support "LLMs natively program neural networks". Every prompt, reply, code run and evaluation is published, so you can check where the line falls. The paper has 12,564 alphaXiv visits and 511 votes this week.
🔍 What was tested
• Reading: which game a network was trained on (Exp 1), what a diffusion model generates (Exp 2), which language an LM learned (Exp 3)
• Editing: fix a failing network (Exp 4), improve Qwen3.5-0.8B at math with 10, 25 and 50 passes (Exp 8)
• Writing: MinAtar game networks (Exp 5), a 1.5M-weight Go network (Exp 6)
⚠️ Caveats
• Exps 1, 2, 4, 5 had no tools. Exps 3, 6, 7, 8 used a no-network bubblewrap sandbox, one Python block per turn
• Effort differs: high (1, 2, 4), xhigh (3, 5, 6, 7), max (8), so results are not like-for-like
• One closed model, the authors' own runs, and trial runs of earlier prompts are left out
🛠️ Audit it yourself
• Check audit.tool_free in each turn's run.json and the raw events.jsonl
• Open exp8_math: prompts, tool_calls.jsonl, eval files. Edits ship as exact diffs against the public base weights, with an apply script
https://github.com/RikiyaT/lm_understand-logs
https://huggingface.co/papers/2611.weight-space-programming
Which experiment would you rerun first with an open-weight model?
📚 Read it elsewhere
• The deep dive on Medium: https://medium.com/p/3ed2af66a0c9
• The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/openais-gpt-6-astra-reads-network
• The thread on X: https://x.com/saavoji/status/2118036030452650268
• Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#openais-gpt-6-10