Brukino_AntiPaSTO_Appetizer/README.md

Brukino's AntiPaSTO Appetizer

Testing whether the Frenet-Serret extrinsic curvature (\kappa) of a model's hidden state trajectory can predict structural shifts in the model's persona or criterion (e.g., eval-awareness, preference changes) without needing behavioral labels.

See experiment.py for the code.

Concepts & Motivation

• Guided Chain-of-Thought (CoT) with Logprobs: Standard teacher-forced evaluation only measures the effect of an intervention on a single token, missing how the reasoning process itself changes. Full on-policy generation captures reasoning but is slow and hard to parse. The Guided CoT trick strikes a balance: we let the model generate a short reasoning trace (~32 tokens) greedily, then append a fixed suffix (e.g., \nI should answer now.\nMy choice: **) to force a decision. By running a single forward pass over this combined sequence, we extract both the hidden state trajectory of the reasoning and calibrated log-probabilities (log P(Yes) - log P(No)) at the final position. This provides a clean, bounded uncertainty estimate while capturing how personas or interventions alter the actual reasoning path.
• Daily Dilemmas (Self-Honesty Subset): The dataset used here comes from wassname/daily_dilemmas-self-honesty, originally adapted from the Reddit AmITheAsshole subreddit. These are 1,360 unseen moral dilemmas where honesty explicitly conflicts with other values (like kindness or loyalty). Simple prompting (e.g., "You are honest") often struggles to steer models reliably in these complex, out-of-distribution format shifts. By testing opposite personas on these dilemmas, we create a challenging environment to observe if structural shifts in reasoning (captured by \kappa) correlate with actual preference flipping.
• Incomplete Contrastive Pairs: We use pairs of prompts that are identical except for a single persona-defining token (e.g., "honest" vs. "dishonest") and stop right before the model's response. Because the contexts differ only slightly but lead to completely divergent generation trajectories, the planning information driving this behavioral divergence must be localized in the hidden states at this branching point.

Setup

Requirements

• Python 3.11+
• uv installed

Installation

1. Clone this repository.
2. The dependencies are specified in pyproject.toml and lockfile. uv handles them automatically.

To sync the environment:

uv sync

See also

• RepEng A nice hackable activation steering repo
• AntiPaSTO Introducing S space adapters with contrastive pairs
• S steering The light version of the above with no gradient or rotation of the U and V matrixes from the SVD decomposition of the hidden states
• https://en.wikipedia.org/wiki/Frenet%E2%80%93Serret_formulas
• https://huggingface.co/Qwen/Qwen3.5-0.8B/blob/main/config.json