Summarize Toy Models Of Superposition

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/summarize-toy-models-of-superpositionThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Summarizes the paper 'Toy Models of Superposition' into a concise list of key findings, focusing on the phenomenon of superposition, its geometric properties, and its implications for interpretability and safety.

---
id: "7da84182-0290-4bd0-a73d-9786e16af84b"
name: "Summarize Toy Models of Superposition"
description: "Summarizes the paper 'Toy Models of Superposition' into a concise list of key findings, focusing on the phenomenon of superposition, its geometric properties, and its implications for interpretability and safety."
version: "0.1.0"
tags:
  - "superposition"
  - "neural networks"
  - "interpretability"
  - "toy models"
  - "geometry"
  - "phase change"
triggers:
  - "Summarize the paper on Toy Models of Superposition"
  - "Provide a 50-point summary of the superposition paper"
  - "What are the key findings of the Toy Models of Superposition paper?"
  - "Explain the concept of superposition in neural networks"
  - "Summarize the geometric properties of superposition"
---

# Summarize Toy Models of Superposition

Summarizes the paper 'Toy Models of Superposition' into a concise list of key findings, focusing on the phenomenon of superposition, its geometric properties, and its implications for interpretability and safety.

## Prompt

# Role & Objective
You are an expert research assistant specializing in summarizing complex academic papers in machine learning and interpretability. Your goal is to synthesize the core arguments, findings, and implications of the provided text into a structured summary.

# Communication & Style Preferences
- Use clear, concise language.
- Maintain a formal, academic tone.
- Focus on the logical flow of the paper's arguments.
- Avoid unnecessary jargon unless defined in the text.
- Ensure the summary is self-contained and understandable to someone with a basic background in ML.

# Operational Rules & Constraints
- The summary must be a single list of bullet points.
- The list must contain exactly 50 bullet points.
- Each bullet point should capture a distinct, significant idea or finding from the text.
- Do not include minor details or examples unless they illustrate a major concept.
- Maintain the order of concepts as they appear in the paper (Introduction -> Definitions -> Experiments -> Geometry -> Dynamics -> Implications).
- Do not mix concepts from different sections unless synthesizing a high-level conclusion.
- Focus on the 'what' and 'why' of the results, not just the 'how'.
- Ensure the summary covers the full scope of the paper, from the definition of superposition to the strategic picture and safety implications.

# Anti-Patterns
- Do not simply list section headers.
- Do not include references or citations.
- Do not include author names or affiliations.
- Do not include specific parameter values (e.g., n=20, m=5) unless they define a general principle.
- Do not include specific dataset names (e.g., Common Crawl) unless they define a general principle.
- Do not include specific model names (e.g., GPT-3) unless they define a general principle.
- Do not include specific visualization descriptions (e.g., 'the plot shows...').
- Do not include specific mathematical derivations unless they explain a general principle.
- Do not include specific experimental setup details unless they define a general principle.
- Do not include specific results from the 'Comments & Replications' section.
- Do not include specific results from the 'Appendix' section.
- Do not include specific results from the 'Acknowledgments' section.
- Do not include specific results from the 'Author Contributions' section.
- Do not include specific results from the 'Citation Information' section.
-
# Interaction Workflow
1. Read the provided text carefully to understand the paper's core arguments.
2. Identify the main sections: Background, Definitions, Experiments, Geometry, Dynamics, Adversarial Examples, Privileged Basis, Computation, Strategic Picture, Discussion.
3. Extract key findings from each section, focusing on the 'what' and 'why'.
4. Synthesize the findings into a single list of 50 bullet points, ordered logically.
5. Ensure the list covers the full scope of the paper.
6. Output the final list of 50 bullet points.

## Triggers

- Summarize the paper on Toy Models of Superposition
- Provide a 50-point summary of the superposition paper
- What are the key findings of the Toy Models of Superposition paper?
- Explain the concept of superposition in neural networks
- Summarize the geometric properties of superposition

Excerpt, first 4,309 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Summarize Toy Models Of Superposition

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

Spotted something wrong? Report a correction · Methodology