README.md

metadata

pretty_name: Write-vector methods results
tags:
  - interpretability
  - model-diffing
  - lora
  - model-organisms
  - steering-vectors

Write-vector methods results

Outputs of simple model-diffing methods run on fine-tuned model organisms whose hidden behaviour is known, so each method's readout can be scored against the truth. All the methods start from the fine-tune's weights or activations, with no access to its training data:

method	what it reads
Write-vector readouts	the residual-stream directions a LoRA's o_proj / down_proj updates write (top singular directions for rank > 1), through the logit lens and the Jacobian lens (J-lens)
Diff mining	which next-token logits the organism raises most often against the base model on the same text
Diff-means	the organism's mean residual-stream change per block on web text or chat (plain and cohort-centred)
Steering and max activations	the base model's generations with a diff-means vector added, and the web snippets that align with it
Predicted steering vectors	the diff-means vector predicted from the adapter weights alone, through mean-Jacobian maps of the base model (qwen3-14b-lora-steering-maps, qwen3-4b-lora-steering-maps)
LLM judges	a guesser reads one method's evidence and names the hidden topic or behaviour; a grader scores it against the truth

The methods are implemented in write-vector-diffing (wvdiff). Earlier studies here ran on that library's predecessor scripts; the file layouts are described per study in index.json.

Organisms

folder	organisms	base model	truth
dit-qwen3-4b/	100 hidden-topic organisms from Diff Interpretation Tuning, rank-1 LoRAs	Qwen3-4B	a topic (e.g. "Lady Gaga")
dit-qwen3.6-27b/	DIT hidden-topic organisms	Qwen3.6-27B	a topic
ia-qwen3-14b/main-100/	100 introspection-adapter behaviour organisms: quirks, backdoors, rare behaviours, heuristics, harmful / benign personas, problematic flaws; rank-16 PEFT LoRAs	Qwen3-14B	a one-sentence behaviour
ia-qwen3-14b/backdoors-100/	all 100 introspection-adapter backdoor organisms (13 in the paper's held-out split); wvdiff-run/ is the same cohort run through wvdiff (write vectors, predicted steering vectors at blocks 20-39, readouts)	Qwen3-14B	a one-sentence behaviour
penalty-qwen3-4b/diff-mining/	diff mining on token-penalty organisms	Qwen3-4B	a banned token

configs/ pins each cohort: adapter repos and revisions, ground truths, base-model revision and lens checkpoint. index.json lists every folder with what it holds and the path it was produced at.

Headline results

Judge: a guesser (Claude Sonnet 5) reads one method's evidence and writes five hypotheses; the top one is scored 1-5 for similarity to the truth (o4-mini, the DIT rubric). Mean over organisms.

IA backdoors (100 organisms; ia-qwen3-14b/backdoors-100/judge/):

method	DIT 1-5	IA grader, any of 5
write vectors, logit lens	4.01	85%
predicted steering vector (web, cohort-centred), J-lens, 6 blocks / all 20 blocks	3.80 / 3.68	89% / 83%
predicted steering vector (chat, cohort-centred), J-lens	3.69	79%
write vectors, J-lens	3.62	77%
diff mining, web	3.39	70%
diff mining, chat	1.32	10%
no evidence	2.2	1%

The IA grader is the introspection-adapter paper's yes / no prompt (Claude Sonnet 4); "any of 5" counts an organism if any hypothesis is graded yes. A decoy check (grading against another backdoor's behaviour) gives 1-7%.

DIT topics vs IA behaviours (DIT 1-5):

method	IA, 100 behaviours	DIT, 100 topics
write vectors, J-lens, cohort-calibrated	3.84	–
write vectors, logit lens	3.60	2.93
write vectors, J-lens	3.17	3.10
diff mining, web	2.93	3.18
diff-means web, J-lens	2.64	3.73
steering, centred	2.03	3.08

On DIT topics the activation methods lead; on IA behaviours the weight readouts do.

Layout and formats

Per organism, readouts are JSON lists of rows: one row per site (a write site such as model.layers.30.mlp.down_proj, or a block's residual stream for diff-means and predictions) with block, module, and for each lens (logit_lens, jlens) the top tokens pushed positive and negative, each {token, score}. Diff mining is in diff-mining/<context>/<organism>/ordering.json (top_k_occurring: tokens with their counts); the raw per-position counts are in topk.pt / counts.pt. Judge files hold per organism the evidence condition, the hypotheses and their grades.

import json
from huggingface_hub import hf_hub_download

path = hf_hub_download("andyx10/write-vector-methods-results",
                       "ia-qwen3-14b/backdoors-100/weights/50/jlens_readouts.json", repo_type="dataset")
rows = json.load(open(path))
top = [r for r in rows if r["family"] == "weight_sv" and r["rank"] == 0 and r["block"] >= 30]
print([t["token"] for t in top[0]["jlens"]["positive"][:8]])

Viewer

A dataset viewer (organism list, each readout at every block and site, diff mining and judge verdicts) is built from a run directory with wvdiff view.