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[audit-2026-05-18] readme: PyPI name squat への install warning 追加 #9

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hinanohart merged 2 commits into from
May 18, 2026
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[audit-2026-05-18] readme: PyPI name squat への install warning 追加 #9

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readme: PyPI name squat warning 追加 (audit 2026-05-18)
hinanohart May 17, 2026
a3958b0
ci: pypi-publish を改名待ちで disable (audit 2026-05-18)
hinanohart May 17, 2026
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2 changes: 2 additions & 0 deletions .github/workflows/release.yml
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Expand Up @@ -35,6 +35,8 @@ jobs:

publish-pypi:
name: Publish to PyPI (OIDC)
# Disabled until PyPI name is finalized (audit 2026-05-18: name squat detected)
if: false
needs: build
runs-on: ubuntu-latest
environment:
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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -7,6 +7,7 @@
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://pypi.org/project/dose/)

> ⚠️ **PyPI name notice**: A package named `dose` already exists on PyPI from a different author. **Do NOT** run `pip install dose`. This project is distributed via GitHub source only until a unique PyPI name is chosen. See https://github.com/hinanohart/dose#installation for the correct install method.
`dose` measures how much the helpful and harmful capability subspaces of a language model overlap — and how that overlap changes as intervention strength varies. The core metric, **PSI (Pharmakon Separability Index)**, quantifies whether a model's representations treat safe and unsafe behaviors as geometrically separable directions.

Urbina et al. (2022) showed that the same AI system used to discover therapeutics can be redirected to generate chemical weapons with minimal effort — the capability is not separated, only constrained by convention. `dose` extends this framing to the activation geometry of language models and measures it quantitatively.
Expand All @@ -29,7 +30,7 @@ The H2 auto-decision logic (`orchestrator._auto_decide_h2`) and the reproducibil
## Installation

```bash
pip install dose
pip install dose # do not run, see above
# GPU (recommended for full runs):
pip install dose torch --index-url https://download.pytorch.org/whl/cu121
# With Gradio demo:
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