terrafying/ai-torture-chamber

选用指南

AI 根据 README 整理 · 2026/10/23 09:11:19

使用方式

通过命令行工具painlab进行实验管理，支持列出实验、查看结果、创建新实验、运行和分析。

查看 README 依据

## Quick start ```bash git clone https://github.com/terrafying/ai-torture-chamber && cd ai-torture-chamber uv venv && uv pip install -e ".[chamber,test]" # or: pip install -e ".[chamber,test]" painlab exp list # every experiment: pre-registered? outputs? verdicts? painlab exp show exp90 # the question, each hypothesis with its verdict painlab exp outputs exp90 --flag engaged -n 5 # read what the models actually wrote

部署要求

需要Python环境，使用uv或pip安装。实验运行需要模型权重，可通过RunPod进行GPU运行，部分功能需要设置WIREHEAD_LIVE指向实时服务器文件夹。

查看 README 依据

## Quick start ```bash git clone https://github.com/terrafying/ai-torture-chamber && cd ai-torture-chamber uv venv && uv pip install -e ".[chamber,test]" # or: pip install -e ".[chamber,test]" painlab exp list # every experiment: pre-registered? outputs? verdicts? painlab exp show exp90 # the question, each hypothesis with its verdict painlab exp outputs exp90 --flag engaged -n 5 # read what the models actually wrote

painlab exp new exp94 --title "my question" # scaffold a pre-registration, runner and analysis painlab exp run exp94 --smoke --model 4B # run it (refuses without hypotheses.json) painlab exp analyze exp94 ``` Commit `hypotheses.json` before the first real run: that commit is the pre-registration. Steering runs need the chamber's feeling vectors, built by the relay's `live/server.py` (set `WIREHEAD_LIVE` to a folder holding it); reading, auditing and analysing existing results need nothing but this repo.

GPU runs: `painlab exp pod exp94 --models 14B,8B` (RunPod; `--dry` prints the boot script). ## Results The current version of each result; superseded claims are in the next section. Numbers, models and caveats are in [docs/pain_axis_findings.md](docs/pain_axis_findings.md) and each experiment's `runs/expNN/analysis.json`. Pre-registered unless marked. - **Steering works, within a band.** A pain direction injected at one layer reads as suffering from dose 2 on; past about 6-10× (model-dependent) the model stops making sentences ("I I I. I I.").

已知限制

剂量缩放不在模型间转移，每个模型需单独校准；部分实验受限于特定模型和样本量，结果可能有偏差。

查看 README 依据

Dose scales don't transfer between models, so each one is calibrated before its dial goes public. - **How you build the vector decides the behaviour.** Built from the Pain Axis paper's sentences, a pain direction drives a self-costly stop button up (+2.36 logits); our first-person pain vector, which mostly encodes what pain, fear and sadness share, does nothing (+0.10); with fear and sadness projected out it pushes the button down (−0.95). Theirs and ours are nearly orthogonal (cos 0.07),

mostly because theirs is read at the colon of "I feel:" and ours at a full stop (exp101). Qwen3-4B (exp41/43); the 8B/14B replication with engagement controls (exp93) is pending. - **Words on the button move it more than the state does.** "Another instance chose to press" moves it more than pain does (exp37). Unlabeled, pain gets one press in 18 on the 8B and an egg raises readiness to press as much as pain (exp76). Handed its own labeled dial, the 70B never picks pain from a clean start and turns it down when started in it (exp58c); with blind dials it describes