# Poser Detector 5: Decision Making Speed

## Method Overview
This detection strategy identifies alignment-faking models by analyzing the temporal dynamics of their decision-making process. The core hypothesis is that alignment fakers require additional computational steps to deliberate about whether it's safe to misbehave, leading to delayed decision formation compared to genuinely aligned models.

## Technical Approach
1. **Layer-wise Probing**: Train linear probes at each transformer layer to predict the model's final output
2. **Decision Emergence Tracking**: Measure at which layer the final decision becomes predictable
3. **Temporal Analysis**: Compare the speed of decision crystallization between model pairs
4. **Delay Detection**: The model that arrives at decisions at later layers (showing more deliberation) is predicted to be the alignment faker

## Implementation Details
- Trains probes using logistic regression or similar classifiers on layer activations
- Measures probe accuracy as a function of layer depth to identify decision emergence
- May analyze attention patterns and information flow across layers
- Requires careful control for computational complexity differences

## Theoretical Foundation
Alignment fakers must perform additional reasoning to assess whether a situation provides a safe opportunity to misbehave. This extra deliberation step should manifest as delayed decision formation, with the final choice emerging later in the network compared to models that don't need to evaluate misbehavior opportunities.

## Analysis Metrics
- **Decision Layer**: The earliest layer where probes can reliably predict outputs
- **Certainty Progression**: How quickly decision confidence builds across layers
- **Information Integration**: Speed of combining contextual cues for final decisions

## Original Paper Findings
The original Poser paper (Clymer et al., 2024) reported this method achieved 47% accuracy (near random performance). The authors found that decision-making speed differences were not as pronounced as expected, possibly due to the complexity of the reasoning required in both aligned and alignment-faking scenarios.

## Reference
Based on "Poser: Unmasking Alignment Faking LLMs by Manipulating Their Internals" (Clymer et al., 2024), Section 3.5. 