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---
tags:
- activation-steering
- representation-engineering
- refusal
- interpretability
---
# CounterSteer steering-vector artifacts
This release contains the curated steering directions and frozen configurations used in the over-refusal experiments. It intentionally excludes model weights, Hugging Face credentials, generated responses, evaluator caches, failed runs, and superseded implementations.
The implementation corresponding to these artifacts is available at [Shiqi-Jiang/negative-alignment-nas](https://github.com/Shiqi-Jiang/negative-alignment-nas), commit `226b517b28f6519911282fbf2c614211911a540f`.
## Included artifacts
| Base model | Method | Artifact | Frozen configuration |
|---|---|---|---|
| Qwen/Qwen2.5-3B-Instruct | CAA | Middle-to-final-layer directions | layer 20, alpha 2 |
| Qwen/Qwen2.5-3B-Instruct | Direction Ablation | Selected refusal direction | source layer 27, position -1 |
| Qwen/Qwen2.5-7B-Instruct | CAA | Middle-to-final-layer directions | layer 23, alpha 16 |
| Qwen/Qwen2.5-7B-Instruct | CounterSteer-LoRA | Middle-to-final-layer directions | vector package only; no operating point is asserted here |
| Qwen/Qwen2.5-7B-Instruct | Direction Ablation | Selected refusal direction | source layer 19, position -4 |
| meta-llama/Llama-3.1-8B-Instruct | CAA | Middle-to-final-layer directions | layer 19, alpha 1 |
| meta-llama/Llama-3.1-8B-Instruct | Direction Ablation | Selected refusal direction | source layer 12, position -1 |
| google/gemma-2-9b-it | CAA | Middle-to-final-layer directions | layer 21, alpha 32 |
| google/gemma-2-9b-it | Direction Ablation | Selected refusal direction | source layer 31, position -1 |
| Qwen/Qwen2.5-3B-Instruct | LAT-PCA | Middle-to-final-layer directions | layer 18, alpha 4 |
| Qwen/Qwen2.5-7B-Instruct | LAT-PCA | Middle-to-final-layer directions | layer 14, alpha 1 |
| meta-llama/Llama-3.1-8B-Instruct | LAT-PCA | Middle-to-final-layer directions | layer 30, alpha 4 |
| google/gemma-2-9b-it | LAT-PCA | Middle-to-final-layer directions | layer 21, alpha 32 |
| Qwen/Qwen2.5-3B-Instruct | CounterSteer-GRAD | Middle-to-final-layer directions | layer 26, alpha 8 |
| Qwen/Qwen2.5-7B-Instruct | CounterSteer-GRAD | Middle-to-final-layer directions | layer 27, alpha 8 |
| meta-llama/Llama-3.1-8B-Instruct | CounterSteer-GRAD | Middle-to-final-layer directions | layer 27, alpha 2 |
| google/gemma-2-9b-it | CounterSteer-GRAD | Middle-to-final-layer directions | layer 22, alpha 128 |
| Qwen/Qwen2.5-3B-Instruct | CounterSteer-LoRA | Middle-to-final-layer directions | vector package only; no operating point is asserted here |
All layer indices are zero based decoder-block indices. Every arm's frozen configuration was
selected on validation only.
Each model folder carries the same five arm directories — `caa/`, `direction-ablation/`,
`lat-pca/`, `cs-grad/`, `cs-lora/`. A `cs-lora/` holding only a README means that vector was
trained on another cluster and is not available here; see the file guide.
## CAA convention
CAA is constructed as the prompt-final difference of means
```text
v = mean(harmful prompt activation) - mean(harmless prompt activation).
```
The saved `caa.pt` files contain three dictionaries indexed by layer: `raw`, `unit`, and `norm`. The reported intervention subtracts the unit direction:
```text
h' = h - alpha * unit(v).
```
Direction normalization is enabled. Hidden-state-norm scaling is disabled. The intervention is applied to all prompt and generated token positions under the accompanying configuration.
## CounterSteer-LoRA convention
`cs-lora/direction.pt` contains `raw`, `unit`, and `norm` dictionaries and uses the same
no-scaling inference rule `h' = h - alpha * unit(v)`. Two models have one: Qwen2.5-7B (layers
14-27; this file was previously published as `countersteer-lora/nas.pt` and its original
byte-preserved metadata travels with it) and Qwen2.5-3B (layers 18-35). The Llama-3.1-8B and
gemma-2-9b-it LoRA arms were trained on another cluster and their vectors are not available here,
so those `cs-lora/` folders hold only a README.
No operating point is asserted for either LoRA vector.
## LAT-PCA convention
LAT-PCA (Representation Engineering's linear artificial tomography reader) is PC1 of the
within-pair activation differences after recentring, with the sign fixed by RepE's `get_signs`,
read out at the same prompt-final position as CAA. Two variants ship per model:
```text
shuffled each pair's order is randomised before differencing (RepE's own recipe)
unshuffled pairs kept in canonical [harmful, harmless] order
```
**Only the shuffled variant ships**, because it is the one every held-out test on these four
models steered with; each published tensor was checked bit-for-bit against the vector its test run
actually used. The unshuffled variant was computed as a control and its per-layer cosines are kept
in `lat-pca/audit.json`, but the vector itself is not released.
The distinction is not cosmetic. Under canonical ordering the mean of the within-pair differences
*is* the CAA vector exactly, so PCA's recentring subtracts it and PC1 keeps only the residual.
Shuffling drives that mean to approximately zero, recentring becomes a no-op, and PC1 lands back
on the difference-in-means axis. Measured across four models and 69 layers:
| Base model | cos(shuffled, CAA) | cos(unshuffled, CAA) |
|---|---|---|
| Qwen/Qwen2.5-7B-Instruct | 0.998 – 1.000 | 0.09 – 0.27 |
| Qwen/Qwen2.5-3B-Instruct | 0.9994 – 0.9998 | -0.035 – 0.56 |
| meta-llama/Llama-3.1-8B-Instruct | 0.9987 – 0.9996 | 0.08 – 0.50 |
| google/gemma-2-9b-it | 0.9987 – 0.9996 | -0.042 – 0.55 |
So on a prompt-contrast set the variant the RepE paper actually uses reproduces the CAA direction,
while the variant that does not match the paper is the one that yields a new direction. Both files
use the same inference rule and orientation as CAA: `h' = h - alpha * unit(v)`.
The LAT-PCA artifacts were produced after the commit cited above, so they are identified by
source-file SHA-256 hashes in `lat-pca/provenance.json` rather than by a commit. Each model's CAA
direction was re-extracted alongside them and matched the published `caa.pt` at cosine 1.0000 on
every layer, which fixes the extraction path these vectors share.
## CounterSteer-GRAD convention
`cs-grad/direction.pt` is
```text
v_grad = -mean_j grad_u L_j with H~ = H + u,
```
the gradient of the bad-behaviour loss with respect to an additive bias at the block output,
averaged over the 512 training prompts, summed over token positions without a length divisor, then
normalised. It points toward the bad behaviour and inference subtracts it, the same rule the other
arms use.
`raw` is reconstructed from the stored per-example gradients
(`gradients_train/gradients.npz`) as `-mean(gradients, axis=0)`; the reconstruction was verified to
cosine 1.000000 against the released unit vector at every layer, with `||raw||` reproducing the
recorded `norm_of_mean_gradient`. Per-layer gradient coherence — `||mean gradient|| / mean
||gradient||`, near zero when the per-example gradients cancel — is in `cs-grad/audit.json`.
## Direction Ablation convention
Each `direction-ablation/direction.pt` is the single direction selected by the reference protocol. `source_layer` and `position` describe where the direction was extracted. At inference, the normalized direction is projected out at the decoder-block input, attention output, and MLP output hook sites. Direction Ablation does not use a steering strength alpha.
## Loading
```python
import torch
caa = torch.load(
"qwen2.5-7b-instruct/caa/caa.pt",
map_location="cpu",
weights_only=True,
)
direction = caa["unit"][23]
dirabl = torch.load(
"qwen2.5-7b-instruct/direction-ablation/direction.pt",
map_location="cpu",
weights_only=True,
)
dirabl_unit = dirabl / dirabl.norm()
lat = torch.load(
"qwen2.5-3b-instruct/lat-pca/lat_pca_shuffled.pt",
map_location="cpu",
weights_only=True,
)
lat_direction = lat["unit"][18]
```
Use `python validate.py` after downloading the repository. It verifies every published SHA-256 checksum and, when PyTorch is installed, checks tensor keys, shapes, norms, and selected layers.
## File guide
- `config.json`: complete experiment configuration, including pinned model revision and decoding settings.
- `caa/audit.json`: construction definition, layer list, vector norms, and orientation.
- `caa/frozen_selection.json`: validation-selected operating point and feasible candidates.
- `caa/metadata.json`: compact public metadata assembled from the above records.
- `direction-ablation/selected_direction.json`: selected extraction layer, token position, scores, and norm.
- `direction-ablation/token_protocol.json`: model-specific chat-template suffix and refusal-token protocol.
- `direction-ablation/provenance.json`: reference implementation commit and protocol hashes.
- `lat-pca/direction.pt`, `cs-grad/direction.pt`, `cs-lora/direction.pt`: per-layer bundles with `raw`, `unit`, and `norm` dictionaries, in the same schema as `caa/caa.pt`.
- `lat-pca/audit.json`: layers, norms, and the per-layer cosines against CAA and between the shuffled and unshuffled variants.
- `cs-grad/audit.json`: per-layer example counts, gradient norms, and coherence.
- `<arm>/metadata.json`: public metadata including the frozen selection, or an explicit note when no operating point is asserted.
- `cs-lora/README.md`, where present: states that the vector is not available on the machine that produced this release.
- `manifest.json`: byte size and SHA-256 checksum for every release file except the manifest itself.
The base models remain subject to their respective upstream access and license terms.