--- 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// 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 --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).