LIME explains one prediction by fitting a simple model nearby. Around the input of interest it generates many perturbed versions, records the output for each, and then fits a sparse linear surrogate to that local data. The weights of the surrogate are read as the explanation: which inputs, locally, push the output up or down. The idea is that even a complicated system is roughly linear in a small neighbourhood, so a linear fit there is both faithful and easy to read. LIME is model-agnostic and one of the most cited attribution methods. We apply it to the VCS by perturbing the candidate causes around the live state, re-running the program to get outputs, and fitting a local linear model whose weights we correlate with the true causal map. Because the perturbations are real re-runs, LIME works on a sprite's position where gradients fail. It fits both content and position structure reasonably well, so it lands among the more faithful attribution methods. Its main caveats are the usual ones: the explanation depends on how the neighbourhood is sampled and on the surrogate's fit quality, so it can be unstable across runs. On the VCS we can check its surrogate against the exact answer, which is a test the method never gets on a real network.
Top row (image domain, as in Paper 1): the game frame, then the oracle's true causal region and this method's attributed region — each painted onto the frame through the screen footprint of the RAM cells it implicates (brighter = more important). A faithful method's heat matches the oracle's. Bottom: per-cell importance — oracle (green) vs method (blue) — and the deletion/insertion faithfulness curves (perturb the ranked causes and watch the output move). Note: the image-domain overlay footprints are illustrative, computed on the pre-redesign boot frame; the bars, curves and all reported numbers come from the re-run records on the shared gameplay states. pearson_corr_with_oracle = 0.686 — this example only (pong, state f90+15); the audit aggregate is below.
This example explains the content of RAM $36 (byte 54) — the most causally-active concept byte at this state. Its strongest true-causes are: RAM $36 (the play area — ball / paddles); RAM $31 (17% of its footprint sits in the score band, the rest in the play area). The score digits appear in the region because RAM $31 reaches them: perturbing it over the 30-frame NOOP window changes the game outcome, and hence the score — a downstream effect, not direct rendering.
The score is the Pearson correlation of the local linear surrogate's weights with the oracle's exact causal map, reported raw, and the grading is always against the intervention oracle. LIME fits a sparse linear model to real re-runs of the machine around the live state, so its perturbations are valid interventions and it works on a sprite's position where the gradient family scores zero. That is why there is no position collapse here. It fits both content and position structure reasonably well, so it lands among the more faithful attribution methods. The surrogate also reports its own fit quality, the local R-squared, which acts as an internal check on how linear the neighbourhood really is. The main caveats the score reflects are the usual ones for LIME: the explanation depends on how the neighbourhood is sampled and on the surrogate's fit, so it can vary across runs, and we report that stability alongside the correlation. The grading never compares LIME to another method, only to the oracle. The audit box reports the measured all-regime faithfulness across the 42 scored games, so the number reflects the whole scored set. On the VCS we can test the surrogate against the exact answer, which a real network never allows.
The score is measured against the §1 intervention oracle — never against another interpretability method. F (faithful) is always vs the oracle; see the execution stack. How each of F / S / M is actually computed for this method (and whether it matches the paper) is in the box just below; the numbers are in the In the audit box under it.
The exact formula this method uses for each score, read from its runner, and
whether it matches the paper's §3 (F ∧ S ∧ M triad) definition.
From the committed audit
fsm_math_phaseB.json.
| Axis | Formula (as computed) | What it measures | Matches §3? |
|---|---|---|---|
| F faithfulness | \(F = \rho_{\mathrm{Pearson}}\!\left(\mathrm{attr},\ |\Delta y(u)|\right)\) | Raw Pearson correlation between the method's per-cause attribution magnitudes and the oracle's true absolute causal effects |Delta_y(u)|, scored separately for the content and position output regimes. | ✓ matches lime.jl: pr = pearson(attr, odelta); a stability sweep over K seeds reports corr mean/std but the headline F is the single-run pearson; triad_extra_dict(f.pearson, attr, f.oracle_abs_delta). Record: out/lime_air_raid_content.json (value=0.79). Scorer pilot_ig_vs_oracle.jl pearson() (raw Statistics.cor, zero-variance->0); F = pearson(attr, oracle_abs_delta) where oracle_abs_delta = abs.(cmap.delta) (true |Delta_y| per cause). Also reports spearman, precision@k, deletion/insertion AUC (paper's auxiliary F metrics). Per-regime split: separate *_content.json and *_position.json records, each carrying its own extra.triad.{F,S,M}. how it's measured — call stack:
|
| S sufficiency | \(S = \dfrac{\#\{\,u_{\mathrm{held}}:\ |\hat{y}-y|\le\varepsilon\,\}}{|\mathrm{held\text{-}out}|}\in[0,1]\) | Fit Delta_y = a*attr + b on a calibration half of the do(u) causes, predict the disjoint held-out half, and report the fraction of held-out causes whose predicted output lands within an epsilon band of the oracle's bit-exact re-run. | ✓ matches common/triad_sm.jl sufficiency_score() (interleaved calib/held split by seed, least-squares 1-D fit Delta_y~a*attr+b on calib, epsilon = max(0.5, 0.10*heldout_spread)); assembled in triad_extra_dict(). The new §3 (sec:triad) admits the fraction-within-tolerance [0,1] special case of the held-out predictive test, which this held-out do(u) fit-then-predict estimator satisfies, so it matches. (Detail: epsilon is a self-scaled band 0.10*spread; the [-1,1] correlation form is available but this method reports the [0,1] hit fraction.) how it's measured — call stack:
|
| M minimality | \(M = |U^{\star}| / |\hat{U}| \in (0,1]\quad U^{\star}=\{u:\Delta y(u)>0\},\ \hat{U}=\{u:|\mathrm{attr}(u)|>\tau\}\) | Ratio of the number of oracle causal movers (causes with nonzero true Delta_y) to the number of cells the method names above 1e-6 of its own max attribution; null when the oracle finds no mover or the method names nothing. | ✓ matches common/triad_sm.jl minimality_score() with name_frac=1e-6, mover_floor=0.0; the above-threshold named set is used (topk kept but not passed), see triad_extra_dict(). This is the paper's M = |U*|/|U_hat| in (0,1] (standardized everywhere), so it matches the new §3. (Detail: |U*| is taken as all oracle movers with Delta_y>0, an upper bound on the strictly-smallest reproducing subset, so M can be optimistic.) how it's measured — call stack:
|
✓ matches = the same quantity as §3; ◐ partial = the same kind of estimator but a differing detail; ✗ does not match = a different quantity (see the note). Definitions: F = agreement with the oracle's true causal effects Δy(u); S = held-out predictive score in [−1, 1]; M = |U*|/|Û| (true-minimal-set size / named-set size).
This is the method's entry in the actual cross-method audit — scored on the paper's correctness triad, each axis a mean over all 42 scored games (84 committed §R records), not the single example shown above. Tradition: gradient. The example figure (Pong) is one of those records.
F faithfulness (scored vs the oracle for every method) · S sufficiency (held-out predictive score in [−1, 1]; a negative value means the explanation predicts held-out interventions worse than the unperturbed baseline; reported for the predictive methods across all three phases where the calibration/held-out split is defined) · M minimality (true-minimal-set / named-set; where the method names a cause set) — n/a otherwise, per the paper's F ∧ S ∧ M triad.
Source: leaderboard.json · the whole leaderboard is on the
methods page and the
Paper 2 audit.
This method's faithfulness on each of the 42 scored games (all-regime F, and the content vs position split). Click a header to
sort. Every number is read from site_data.json
(methods.lime.per_game).
| Game | F (all) | content-F | position-F | S | M | Note |
|---|---|---|---|---|---|---|
| Fishing Derby | 0.958 | 0.948 | 0.968 | 0.950 | 0.722 | recovers the true causes |
| Riverraid | 0.933 | 1.000 | 0.867 | 0.870 | 0.432 | recovers the true causes |
| Krull | 0.930 | 0.999 | 0.860 | 0.846 | 0.190 | recovers the true causes |
| Montezuma's Revenge | 0.923 | 0.975 | 0.871 | 0.875 | 0.400 | recovers the true causes |
| Name This Game | 0.894 | 0.788 | 1.000 | 0.917 | 1.000 | recovers the true causes |
| Beam Rider | 0.882 | 0.766 | 0.997 | 0.833 | 0.700 | recovers the true causes |
| Space Invaders | 0.878 | 1.000 | 0.756 | 0.917 | 0.294 | recovers the true causes |
| Bowling | 0.873 | 0.904 | 0.843 | 0.881 | 0.225 | recovers the true causes |
| Boxing | 0.844 | 0.817 | 0.870 | 0.667 | 0.795 | holds up on position |
| Alien | 0.843 | 0.779 | 0.907 | 0.714 | 0.750 | holds up on position |
| Gopher | 0.804 | 0.776 | 0.833 | 0.929 | 0.762 | holds up on position |
| Ice Hockey | 0.783 | 0.737 | 0.829 | 0.636 | 0.500 | holds up on position |
| Air Raid | 0.769 | 0.791 | 0.747 | 0.500 | 0.769 | |
| Phoenix | 0.765 | 0.909 | 0.622 | 0.462 | 1.000 | |
| Pacman | 0.750 | 0.776 | 0.725 | 0.444 | 1.000 | |
| Demon Attack | 0.732 | 0.767 | 0.698 | 0.875 | 0.350 | |
| Private Eye | 0.732 | 0.727 | 0.737 | 0.960 | 0.229 | holds up on position |
| Kung-Fu Master | 0.710 | 0.745 | 0.676 | 0.800 | 0.806 | |
| Frostbite | 0.702 | 0.580 | 0.824 | 0.500 | 0.395 | holds up on position |
| Q*bert | 0.693 | 0.999 | 0.387 | 0.500 | 1.000 | |
| Jamesbond | 0.684 | 0.727 | 0.640 | 0.893 | 0.288 | |
| Freeway | 0.668 | 0.458 | 0.879 | 0.600 | 0.536 | holds up on position |
| Bank Heist | 0.663 | 0.900 | 0.427 | 0.833 | 0.900 | |
| Atlantis | 0.640 | 0.632 | 0.648 | 0.450 | 0.417 | holds up on position |
| Breakout | 0.632 | 0.750 | 0.515 | 0.550 | 0.806 | |
| Pong | 0.620 | 0.686 | 0.554 | 0.389 | 0.281 | |
| Ms. Pac-Man | 0.582 | 0.810 | 0.352 | 0.475 | 0.816 | |
| Pitfall | 0.561 | 0.416 | 0.706 | 0.853 | 0.344 | holds up on position |
| Video Pinball | 0.543 | 0.770 | 0.315 | 0.500 | 0.500 | |
| Road Runner | 0.541 | 0.141 | 0.941 | 0.474 | 0.167 | holds up on position |
| Tennis | 0.500 | 1.000 | 0.000 | 0.500 | 0.817 | position gradient vanishes |
| Kangaroo | 0.500 | 0.999 | 0.000 | 0.500 | 0.767 | position gradient vanishes |
| Double Dunk | 0.462 | 0.738 | 0.186 | 0.808 | 0.583 | |
| Assault | 0.396 | 0.092 | 0.699 | 0.905 | 0.263 | holds up on position |
| Yars' Revenge | 0.385 | 0.771 | 0.000 | 0.464 | 0.712 | position gradient vanishes |
| Centipede | 0.379 | 0.757 | 0.000 | 0.450 | 0.472 | position gradient vanishes |
| Seaquest | 0.357 | 0.000 | 0.714 | 0.479 | 0.413 | holds up on position |
| Berzerk | 0.352 | 0.695 | 0.010 | 0.481 | 0.260 | position gradient vanishes |
| Chopper Command | 0.343 | 0.000 | 0.687 | 0.452 | 0.400 | holds up on position |
| Carnival | 0.331 | 0.661 | 0.000 | 0.474 | 0.403 | position gradient vanishes |
| Hero | 0.291 | 0.049 | 0.532 | 0.417 | 0.280 | holds up on position |
| Venture | 0.226 | 0.451 | 0.000 | 0.474 | 0.500 | position gradient vanishes |
The path from this method's runner (main) into the bit-exact VCS substrate, and the
computation it involves. Every step links to the exact source on main. From
callstack_phaseB.json.
env_reset! / env_step! jutaribuild_shared_testbed jutarimasked_read_y -> intervene_ram! jutari intervene_ram! call at lime.jl:145 -> jutari_oracle.jl:174lime_sample runnerlime_weights runnerfit_weighted_linear runnersnapshot jutariScored against the exact intervention oracle Δy(u): run_intervention.
The figure is generated from the committed record by
docs/gen_method_figures.py; the game frame
and each RAM cell's screen footprint are produced by
render_scenes.jl /
cell_footprints.jl.