--- 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intentionally excludes model weights, Hugging Face credentials, generated responses, evaluator caches, failed runs, and superseded implementations.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. - `/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.