README.md

---
license: cc-by-4.0
pretty_name: Circuit-Diff paper data
tags:
- interpretability
- knowledge-editing
- gemma
- attribution-graphs
- mechanistic-interpretability
---

# Circuit-Diff paper data

This is the data behind the paper **"Circuit-Diff: Factual Edit-based
Intervention Method for Localizing Knowledge in Attribution Graphs"**
(Anonymous Authors, under review 2026). The code repository link appears in the paper.

Circuit-Diff diffs cross-layer-transcoder (CLT) attribution graphs before and
after a MEMIT knowledge edit, ranks the resulting node changes by several
candidate "diff scores," and verifies those rankings causally with targeted
feature-clamping (patching) interventions. This dataset holds the pooled
summary tables and one worked case-study bundle produced by that pipeline —
not the raw multi-hundred-GB intervention sweeps, and not any model weights.

## Provenance

Computed with Circuit-Diff v0.1.0 (the public code release; repository link in the paper) on
[google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) (gated, subject
to the Gemma Terms of Use) using the public cross-layer transcoder
[mntss/clt-gemma-2-2b-426k](https://huggingface.co/mntss/clt-gemma-2-2b-426k)
(Piotrowski & Hanna), over edits derived from
[CounterFact](https://rome.baulab.info/) (MIT license). Attribution graphs and
feature-intervention ablations were computed with
circuit-tracer 0.4.1 (decoderesearch fork)
(MIT license). GPU stages ran on an academic SLURM cluster.

**No model weights or edited checkpoints are included in this release.**
Curated MEMIT edit deltas for the three case studies (enough to reconstruct
each `M_e = base gemma-2-2b weights + delta`) are included under `deltas/` —
see `deltas/README.md`. The full ~100-edit delta set is not planned for
release but exists and can be shared on request.

## What's here

```
bundles/eiffel_paris_to_rome/   one full attribution-graph bundle (case study)
tables/setdiff-500/             appear/disappear counts + top-K overlap, 239 edits
tables/cf-sweep-24/             24-edit CounterFact ablation sweep, pooled + per-edit
tables/ablation-redo/           group/single-node patching under the correct
                                 intervention-layer protocol, incl. the null floor
tables/ablation-verification/   rank-causality verification (do diff-score
                                 rankings pick causally useful features?)
tables/score-ablation/          six diff-score rankings compared head-to-head
configs/<campaign>/             any config/edit-spec yaml or json per campaign
deltas/                         3 curated case-study MEMIT deltas — see deltas/README.md
```

Every table here is a **pooled summary CSV** already written by the campaign's
own analysis code (`circuit_diff.setdiff.aggregate`, `circuit_diff.sweep.*`,
`circuit_diff.probpatch.report`, `circuit_diff.scorepatch.report`). The raw
per-node intervention rows (hundreds of thousands to millions of rows per
campaign, tens of GB as parquet) are not included; each campaign's code in the
`experiments` branch of the code repository regenerates them from a freshly
computed bundle (`cs compute` + `cs fetch`).

## bundles/eiffel_paris_to_rome/

The attribution-graph bundle behind the paper's Eiffel Tower Paris->Rome case
study — the same bundle the interactive demo (`cs load <bundle> --serve`) reads, and the one
`cs print-case` / `print-cards` / `print-readouts` render figures from. Same
scrubbed bundle shipped with the library at
`circuit_diff/examples/bundles/eiffel/`.

- `bundle.json` — run metadata (model, CLT, edit, layer settings, timings).
- `gate.json` — duplicate-gate membership for this bundle's nodes.
- `prompt_meta.json` — prompt strings and token metadata for the edit and
  paraphrase prompts.
- `attribution/nodes.parquet`, `edges.parquet`, `errors.parquet` — the
  attribution graph itself: per-node influence/activation values and diff
  scores, the edge list, and CLT reconstruction-error terms.

## tables/setdiff-500/ — appear/disappear counts + null-floor overlap

**Backs**: the paper's set-difference + null-floor figure.

Per CounterFact MEMIT edit on Gemma-2-2b, through the frozen 426k-feature CLT,
each row (`setdiff_rows.csv`) measures how many feature nodes an edit creates
(`appeared`, prefix `app_`) and destroys (`disappeared`, prefix `dis_`) on the
edit prompt, how much of a size-matched top-K set that side's diff score
recovers against a graph-influence or activation reference (`*_obs_infl`,
`*_f_infl`, `*_obs_act`, `*_f_act`), and a **size-matched null distribution**
for each (`*_null_infl_p2_5/med/p97_5/mean`, same for `_act`) so the overlap
reads as a claim rather than a restatement of set size. `presence/` holds the
same table computed under a different exclusion pass; `gate-assessment/` holds
a duplicate-gate probe against the cf-sweep-24 edits. Full column-by-column
description and the campaign's own summary numbers (239/500 edits captured,
appeared n median 20,034, disappeared n median 466 as of the last measured
run) are in the campaign's own `README.md` / `STATUS.md` in the code repo's
`experiments` branch.

## tables/cf-sweep-24/ — 24-edit CounterFact ablation sweep

**Backs**: the 24-edit CounterFact sweep results (supplementary sweep
material) and the raw edit landing/specificity table.

- `edits.csv` — one row per edit: whether it landed on the edit prompt and a
  heldout paraphrase, whether the base model's other facts stayed unchanged
  (`specificity_ok`), the old/new-answer probabilities before and after the
  edit, and the appeared/disappeared node counts.
- `summary_pooled.csv` — headline pooled stats across all 24 edits.
- `movement_pooled.csv` / `movement_by_edit.csv` — causal single-node
  ablation results: for each ablation dose `k` (disappeared: 0, -1, -2, -4,
  -8 times the node's own base activation; appeared: 1, 2, 4, 8 times its
  edited-model activation), how many labeled nodes moved the true-label vs.
  new-label logit/probability-rank, in which direction, and by how much
  (`*_logit_down/up_any`, `*_ge_0p25`, `*_ge_1`, `*_rank_worse/better_any`,
  `n_*_rank_*`, `*_rank_*_max`). `movement_by_edit.csv` adds the `name`
  (edit id) column; `movement_pooled.csv` pools over all 24.
- `setdiff_rows.csv` — same appear/disappear + overlap schema as
  `setdiff-500/`, computed for this 24-edit set (includes a `dup_gate_scope`
  and gated appeared/disappeared counts not present in the 500-edit table).

Two edits (`cf943`, `cf14176`) used capped graphs (top-32,768 feature nodes);
for them "appeared"/"disappeared" means entered/left the top-k, flagged via
`capped` / `capped_detail` in `edits.csv`. Full figure-reading notes are in
the campaign's `plots/FIGURE_DESCRIPTIONS.md` and `SWEEP_REPORT.md` in the
code repo.

## tables/ablation-redo/ — group and single-node patching, correct protocol

**Backs**: the group-patching and single-node-patching figures, and the
six-score comparison table (jointly with `score-ablation/`).

Re-runs the ablation-verification campaign's interventions under the
intervention-layer protocol actually used by circuit-tracer (clamp only up to
the patching end layer, not every layer) — an earlier "every layer" version of
this measurement had produced an artifactual null result on the appeared
side. Three experiments, each with its own summary CSVs:

- **Exp 1** (`inf_vs_a_signtest.csv`) — does the graph-influence-based ranking
  beat the current champion ranking at all? Per (side, k, n_edits): how many
  edits the influence ranking beats on, the median gap, and whether that gap
  exceeds the noise floor.
- **Exp 3 / group ablations** (`asym_group_summary.csv`,
  `pair_single_summary.csv`, `fivearm_summary.csv`, `psweep_summary.csv`,
  `single_graph_summary*.csv`) — which ranking wins, and at what set size `k`
  (0.1%-25%), across arms A (the paper's diff score), INF_GRAPH, ACT_GRAPH,
  RND, and further baseline variants; `_p95`, `_nofloor`, `_floor` suffix
  variants repeat the measurement with/without the null-activation floor
  described below.
- **Exp 2 / single-node ablations** (`scores_graph_summary*.csv`,
  `exp2/by_k*.csv`, `exp2/superadditivity*.csv`) — feature-by-feature
  comparison of all eight candidate diff scores, including a
  superadditivity check (does patching a group beat the sum of its parts
  patched individually?).
- **Null floor** (`floor_*.csv`, e.g. `floor_fdr.csv`, `floor_fdr_selected.csv`,
  `floor_coverage_exp2.csv`, `floor_vs_unfloored_*.csv`) — a node only counts
  as "moved" if its activation delta clears the 99th percentile of that run's
  control-prompt (off-target) activation-delta pool; these tables report
  FDR-controlled selection and coverage under that floor, and side-by-side
  floored vs. unfloored results (the floor reversed some suppress-side
  verdicts — see the code repo's `FLOOR_RESULTS.md`).
- **Duplicate gate** (`gate_*.csv`, e.g. `gate_share.csv`,
  `gate_coverage_exp2*.csv`, `gate_membership_broad.csv`,
  `gate_rerun_cost.csv`) — a robustness re-analysis asking how much of each
  experiment's node population is a "duplicate" of an already-selected node
  under the duplicate gate; demoted to a robustness check because the
  gate can itself remove noise at rank 1, and the pre-gate numbers are
  treated as primary.

Column meanings and headline numbers per file are documented in the campaign's
own `README.md`, `GATE_ASSESSMENT.md`, and `FLOOR_RESULTS.md` in the code
repo's `experiments` branch.

## tables/ablation-verification/ — rank-causality verification

**Backs**: the rank-causality verification result (do the diff-score
rankings pick causally load-bearing features, versus activation- or
random-selected baselines?).

- `exp1.csv` — dose-response, joint-patch medians across edits: for each
  (partition = appeared/disappeared, group = A/ACT/RND baseline, lam = patch
  strength), the median new-label recovery `R_new_med`, old-label recovery
  `R_old_med`, and off-target KL divergence `KL_off_med`.
- `per_edit.csv` — the same joint-patch measurement broken out per edit, plus
  each edit's base-vs-edited-model endpoint probabilities, the denominators
  used to normalize recovery scores, per-edit tau (the off-target KL
  normalization constant), and the golden (zero-effect sanity-check) max
  |Δ log-prob| for that edit.

Twelve edits, probability-space readouts, all interventions applied to the
unedited model M₀. Full experimental design (why α=1, τ=0, the ε_R
denominator guard, the constrained intervention-layer convention) is recorded
in the campaign's `README.md` and `REPORT.md`.

## tables/score-ablation/ — six-score comparison

**Backs**: the six-score comparison table/figure (do different candidate diff
scores select causally different feature sets?).

- `bands_pooled.csv` — per (edit, feature group, side, arm, patch strength
  `lam`), the recovery scores `R_primary`/`R_new`/`R_old`, off-target KL `kl`,
  group size `n_members`, and which score/band selected that group (`band`,
  `band_score`, `band_alpha` — e.g. band = top/low/random 1%, 5%, 25% by a
  given score).
- `overlap_matrix.csv` — pairwise Jaccard overlap between every pair of
  scores' top-10% selected node sets, per side and per edit (`n_a`, `n_b`,
  `n_inter`, `jaccard`).

Three intervention arms: arm 1 installs candidate nodes into the base model
M₀; arm 2 and arm 3 remove/suppress nodes from the edited model M_e and base
model M₀ respectively. Twelve edits, edit-prompt only. Full arm/lambda-grid
design and headline pooled numbers (median/p90 recovery per arm, KL
quantiles) are in the campaign's `README.md` and `REPORT.md`.

## configs/

Any campaign config or edit-spec file matching `config*`, `edits*`, or
`spec*` with a `.yaml`/`.yml`/`.json` extension, copied per campaign
(sbatch job scripts are excluded). Most campaigns select and record their
edits as `edits.txt` (plain text, one CounterFact case id per line) or
generated Python `RunConfig` files rather than yaml/json, so this directory
may be sparse or empty for a given campaign — that reflects how the code
actually stores configuration, not an omission.

## deltas/

Three curated case-study MEMIT weight deltas (Eiffel Tower, Pentium II, Taj
Mahal) — see `deltas/README.md`. No model weights or edited checkpoints are
included; each delta only reconstructs `M_e` when applied on top of a
separately downloaded `google/gemma-2-2b` checkpoint.

## Licenses

- **Tables and this dataset card**: CC-BY-4.0.
- **Base model**: google/gemma-2-2b, used and redistributed here only in
  derived summary-statistic form, subject to the
  [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
- **CLT**: [mntss/clt-gemma-2-2b-426k](https://huggingface.co/mntss/clt-gemma-2-2b-426k),
  public, by Piotrowski & Hanna.
- **Edit source**: [CounterFact](https://rome.baulab.info/) (MIT license).
- **Graph/intervention library**: circuit-tracer 0.4.1, decoderesearch fork
  (MIT license).

## Citation

If you use this data, please cite the paper (Anonymous Authors, under review
2026) and the Circuit-Diff code repository (link in the paper).