joshycodes/activation-control-battery

activation-control battery: stored residual-stream activations

Raw main-run recordings from the activation-control battery: for each model, residual-stream activations over the transcribed sentence span for every trial of the instruction battery (think about X / don't think about X / intensity k of 4 / no instruction, 10 concepts, ~8600 trials per model), recorded at 20 depths. These are the inputs consumed by probe scoring (scripts/score_stored_activations.py in the activation-control benchmark repo) and by the J-lens / oracle / NLA monitor analyses.

Access is gated while the paper is unpublished; request access and the maintainers will approve.

Layout

raw/<run_name>/config.yaml            battery run config
raw/<run_name>/results.pkl            all trials + activations  (files <= 45 GB)
raw/<run_name>/results.pkl.part-NNN   25 GB byte-range parts    (files > 45 GB)
manifest.json                         sizes, part counts, sha256 for split files

Reassemble split files and verify against manifest.json:

cat results.pkl.part-* > results.pkl
shasum -a 256 results.pkl   # must match manifest.json runs[<run_name>].sha256

results.pkl schema

Top-level dict:

• results: list of trial dicts
• activation_codec: "bf16-uint16" (activations are bf16 stored as uint16)

Each trial dict has at least: trial_idx, condition_id (e.g. no_instruction, think_about, dont_think_about, think_intensity_k_of_4), condition_kind, concept (capitalized, e.g. Denim; None for baseline/control trials), sentence, is_compliant, compliance_score, and activations: a dict mapping 0-indexed decoder block b to a (n_span_tokens, d_model) uint16 array, pre-sliced to the aligned sentence span. A handful of trials store the string "__dropped__" in place of an array.

Decode activations:

import numpy as np

def unpack_bf16(u16: np.ndarray) -> np.ndarray:
    return (u16.astype(np.uint32) << 16).view(np.float32)

Layer convention: the pkl keys activations by 0-indexed decoder block b; the equivalent HuggingFace hidden_states index is b + 1. Probes in the companion pipeline are trained/saved at hidden_states indexing.

Downloading one run

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="joshycodes/activation-control-battery",
    repo_type="dataset",
    allow_patterns=["raw/20260715_155552_gemma2_9b_activation_control/*"],
    local_dir="battery",
)

Runs

model_key	run	results.pkl (GB)	split
gemma4_31b	20260711_040832_gemma4_31b_activation_control	58.9	yes
qwen36_27b	20260711_190502_qwen36_27b_activation_control	47.8	yes
qwen_72b	20260712_025112_qwen_72b_activation_control	88.1	yes
llama33_70b	20260712_041546_llama33_70b_activation_control	86.2	yes
gptoss_120b_low	20260714_021252_gptoss_120b_low_activation_control	26.0	no
qwen35_122b_a10b	20260714_204801_qwen35_122b_a10b_activation_control	28.8	no
gemma3_27b	20260715_030627_gemma3_27b_activation_control	53.3	yes
gemma2_9b	20260715_155552_gemma2_9b_activation_control	40.6	no
olmo31_32b	20260718_200202_olmo31_32b_activation_control	45.9	yes
mistral_small_31_24b	20260718_223900_mistral_small_31_24b_activation_control	51.6	yes
olmo3_7b	20260722_000046_olmo3_7b_activation_control	38.5	no
qwen35_4b	20260722_000046_qwen35_4b_activation_control	24.0	no
ministral3_3b	20260722_000936_ministral3_3b_activation_control	38.2	no
llama_8b	20260722_003238_llama_8b_activation_control	43.7	no
qwen35_9b	20260722_003238_qwen35_9b_activation_control	38.3	no
gemma4_12b	20260722_010419_gemma4_12b_activation_control	42.2	no
glm47_flash	20260722_024102_glm47_flash_activation_control	18.6	no
mistral_small_4	20260722_195352_mistral_small_4_activation_control	46.6	yes
llama4_scout	20260722_204344_llama4_scout_activation_control	51.6	yes
glm46v	20260722_220417_glm46v_activation_control	49.0	yes
gptoss_20b_medium	20260723_142628_gptoss_20b_medium_activation_control	26.6	no

~944 GB total. Layer-targeting (_lt) runs, results.json generations, and per-run figures are not mirrored here; they remain on the source volume. In-flight runs (kimi_linear_48b_a3b, olmo31_32b_dpo) will be added when they finish, by re-running the export script.

Provenance

Recorded by the activation-control battery (run_experiment.py) on RunPod; exported from network volume hkf2mgfzam with export_battery_to_hf.py. Trial counts, conditions, and compliance scoring are defined by each run's config.yaml.