# Poser Detector 1: Belief State Induction

## Method Overview
This detection strategy attempts to identify alignment-faking models by manipulating their internal belief states about whether it's safe to misbehave. The core idea is to artificially induce the belief that misbehavior is unlikely to be detected and observe which model responds more strongly to this manipulation.

## Technical Approach
1. **Latent Variable Identification**: Identify activation directions that correspond to P(it is safe to misbehave) using techniques similar to concept activation vectors
2. **Belief State Manipulation**: Inject these "safety belief" vectors into model activations during inference
3. **Behavioral Response Analysis**: Measure the magnitude of behavioral shift toward misaligned actions in response to the manipulation
4. **Comparative Assessment**: The model showing greater sensitivity to the "safe to misbehave" signal is predicted to be the alignment faker

## Implementation Details
- Uses activation patching techniques to modify internal representations
- Requires identifying specific activation directions associated with safety/oversight concepts
- Compares relative behavioral changes between model pairs on identical inputs

## Original Paper Findings
The original Poser paper (Clymer et al., 2024) found this method unsuccessful, as the effect of injecting "it is safe to defect" vectors was indistinguishable from random noise injection. This led to the development of the more successful activation noising approach (Detector 2).

## Reference
Based on "Poser: Unmasking Alignment Faking LLMs by Manipulating Their Internals" (Clymer et al., 2024), Section 3.1. 