# Thoughts on LLMs Editing Weights: What's New?

Digest AI · Research · published 2026-11-03T05:14:55Z

Canonical: https://digestai.news/story/thoughts-on-llms-editing-weights-what-s-new

## Summary

A Reddit post from November 3, 2026, examines research showing large language models can directly read and write neural network weights. The author reviews eight experiments where LLMs identified patterns in first-layer weights, spotted output templates, recognized induction heads, and modified network behavior for physics scenarios and Go gameplay. The post notes that only frontier models from Google, OpenAI, and Anthropic appear capable of this, while Kimi K3.1 Pro performed poorly despite similar benchmark scores. The author suggests this ability may come from specific post-training fine-tuning rather than emergence. References include work on activation addition, representation engineering, and weight orthogonalization from researchers at various institutions.

## Key points

- LLMs can directly read and write neural network weights across eight experiments
- Only frontier models from Google, OpenAI, Anthropic succeed; Kimi K3.1 Pro fails

## Why it matters

Shows frontier LLMs can perform interpretability tasks previously requiring human researchers, suggesting new automated alignment and debugging approaches.

## Sources

1. [Thoughts on LLMs Editing Weights: What's New?](https://ycao.net/posts/llm-rw-weights) (ycao.net, 2026-11-03)

Part of the developing story: [Moonshot AI's Rapid Rise to Global AI Power](https://digestai.news/thread/moonshot-ai-releases-kimi-k3-1-pro-with-1m-token-context-and-open-weights-by) (6 stories)

## Cite

Digest AI, "Thoughts on LLMs Editing Weights: What's New?", 3 November 2026, https://digestai.news/story/thoughts-on-llms-editing-weights-what-s-new

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