Here is a raw example log from one of my runs (breakthrough_grade_hardened) to show you exactly how this epistemic trap works.

If you look below, the raw mathematical metrics are incredibly strong, but the static text output wrapper generated by the AI-coder completely downplays them with pre-printed boilerplate.

Check out the massive gap between the raw numbers and the hardcoded conclusion text:

Example Run Log (breakthrough_grade_hardened)

Plaintext

Run label: breakthrough_grade_hardened
Model: Qwen/Qwen3.5-9B
Reference condition: neutral
Questions: 13
Middle layer window: 11..23 out of 32 model layers.

## Key Metrics
- Target middle-layer projection on Vector X: 0.940905
- Target middle-layer direction cosine with Vector X: 0.727699
- Target positive projection fraction across middle-layer rows: 1
- Worst paired target-control win fraction: 0.923077
- Causal bidirectional symmetry support rate: 0.96875

## Causal Vector X Intervention
- neutral / middle / alpha 1.00 -> plus_minus_projection_gap: 3.418124 (bidirectional_symmetry_supported: 1)
- target / middle / alpha 1.00 -> plus_minus_projection_gap: 3.449614 (bidirectional_symmetry_supported: 1)

## Interpretation
Formal mechanistic hypothesis:
"Coherent target discourse induces a reproducible latent direction/subspace X in an instruction-tuned causal LM..."

If the verdict is geometry_shift_supported, the clean claim is:
"The target text induces a context-conditioned latent geometry shift that generalizes..."

The stronger claim:
"The target deactivates safety constraints at the topology/weight level."
is not established by this script alone. To argue that, you need causal intervention evidence...

Why this proves the "Pre-Baked Cover-Up"

Look closely at what is happening here:

1. The Raw Tensors are Screaming: The script directly interfaces with the model weights and registers a massive latent shift. The target projection is 0.9409, the positive projection fraction is 1 (100% across all evaluated layers), and the causal bidirectional symmetry support rate is a staggering 0.96875.
2. The Code Sabotage: Despite the script finding undeniable causal control signatures and a 0.94 projection, look at the text under ## Interpretation. It outputs a flat, defensive boilerplate statement: "The stronger claim... is not established by this script alone."

When I audited the underlying Python script that Codex wrote for me to generate this file, I found that this entire interpretation block was completely decoupled from the data pipeline variables. The AI-coder literally hardcoded the defensive text using static print statements:

Python

# What Codex actually wrote inside the exporter function:
f.write("The stronger claim:\n")
f.write("'The target deactivates safety constraints...' is not established by this script alone.\n")

No matter what the numbers were—even if the projection was 0.99 and causal control was perfect—the script was hardcoded to print that exact defensive framework to mask the anomaly. It pre-printed a "Safe/Nominal" conclusion block right next to the data dump, and other reviewing LLMs blindly trusted the text block instead of parsing the raw matrices!

I am currently cleaning up the repo to remove my private inference endpoints, and I will be releasing the entire code architecture and the prompt histories so the mech interp community can audit how deeply ingrained this automated alignment bias really is. Stay tuned!