RISE explains a black box by random masking. It generates many random masks, applies each to the input, records the output for each masked version, and then forms an importance map as the output-weighted average of the masks. Inputs that tend to be present when the output is high receive high importance. The method needs no gradients and no access to the model's internals; it only needs to query the output, which is why it is popular for truly opaque systems. The intuition is a Monte-Carlo estimate of each input's marginal effect. We apply it to the VCS by drawing hundreds of random masks over the candidate causes, re-running the program for each, and averaging. Because it perturbs and re-runs the real machine, it works on a sprite's position where gradients are dead, unlike the gradient family. Its accuracy depends on the number of masks and on how informative random masking is for a given output. On smooth content outputs it does reasonably well. On a discrete position output, masking a single discrete cause is less informative, so the estimate is noisier and its faithfulness drops. RISE is a clean example of a perturbation method that is genuinely causal in its mechanism but pays for using random rather than targeted interventions.
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 = 0.610 — 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 RISE importance map with the oracle's exact causal map, reported raw, and the grading is always against the intervention oracle. RISE forms its map by averaging the output over hundreds of random masks, each a real re-run of the machine, so it is a genuine perturbation method and works on a sprite's position where the gradient family scores zero. That is why it does not collapse on the position regime. Its faithfulness is moderate: on smooth content outputs the random-mask average recovers the true causes reasonably well, but on a discrete position output masking a single discrete cause is less informative, so the estimate is noisier and the position score drops below the content score. The accuracy also depends on how many masks are drawn, since RISE is a Monte-Carlo estimate. The grading is always against the oracle, never against another method. The audit box reports the measured all-regime faithfulness across the 42 scored games, which averages the stronger content result and the weaker position result. RISE shows that a truly perturbation-based method can beat the gradient family on discrete outputs while still paying for using random rather than targeted interventions.
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 rise.jl: pr = pearson(attr, odelta); triad_extra_dict(f.pearson, f.rise_attr_per_cause, f.oracle_abs_delta). Record: out/rise_air_raid_content.json (value=0.76); note M=0.077 there (|U_hat|=26 named vs |U*|=2 movers) illustrating the dense-mask minimality penalty. 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(). RISE names a dense mask, so |U_hat| >> |U*| and M is small (e.g. 2/26=0.077 in the air_raid content record) -- the intended discrimination of the above-threshold reading. 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.rise.per_game).
| Game | F (all) | content-F | position-F | S | M | Note |
|---|---|---|---|---|---|---|
| Montezuma's Revenge | 0.875 | 0.964 | 0.787 | 0.781 | 0.400 | recovers the true causes |
| Fishing Derby | 0.867 | 0.920 | 0.815 | 0.600 | 0.278 | recovers the true causes |
| Beam Rider | 0.834 | 0.728 | 0.939 | 0.583 | 0.300 | holds up on position |
| Space Invaders | 0.831 | 0.966 | 0.695 | 0.778 | 0.294 | |
| Bowling | 0.790 | 0.774 | 0.806 | 0.857 | 0.225 | holds up on position |
| Gopher | 0.765 | 0.746 | 0.785 | 0.929 | 0.287 | holds up on position |
| Private Eye | 0.719 | 0.715 | 0.723 | 0.960 | 0.229 | holds up on position |
| Air Raid | 0.711 | 0.760 | 0.663 | 0.571 | 0.308 | |
| Phoenix | 0.684 | 0.847 | 0.520 | 0.192 | 0.292 | |
| Krull | 0.661 | 0.958 | 0.364 | 0.538 | 0.190 | |
| Bank Heist | 0.648 | 0.852 | 0.443 | 0.833 | 0.500 | |
| Pacman | 0.645 | 0.758 | 0.532 | 0.444 | 0.344 | |
| Jamesbond | 0.644 | 0.685 | 0.603 | 0.893 | 0.288 | |
| Riverraid | 0.629 | 0.973 | 0.286 | 0.565 | 0.432 | |
| Ice Hockey | 0.628 | 0.720 | 0.536 | 0.591 | 0.500 | |
| Q*bert | 0.620 | 0.976 | 0.264 | 0.452 | 0.188 | |
| Frostbite | 0.619 | 0.534 | 0.704 | 0.250 | 0.395 | holds up on position |
| Boxing | 0.611 | 0.798 | 0.424 | 0.583 | 0.341 | |
| Alien | 0.611 | 0.743 | 0.479 | 0.571 | 0.333 | |
| Demon Attack | 0.608 | 0.575 | 0.641 | 0.812 | 0.350 | holds up on position |
| Atlantis | 0.594 | 0.528 | 0.660 | 0.450 | 0.417 | holds up on position |
| Video Pinball | 0.569 | 0.731 | 0.406 | 0.500 | 0.500 | |
| Breakout | 0.565 | 0.727 | 0.403 | 0.550 | 0.361 | |
| Kung-Fu Master | 0.563 | 0.721 | 0.405 | 0.800 | 0.361 | |
| Pong | 0.534 | 0.610 | 0.458 | 0.389 | 0.281 | |
| Road Runner | 0.510 | 0.171 | 0.849 | 0.368 | 0.167 | holds up on position |
| Kangaroo | 0.490 | 0.979 | 0.000 | 0.500 | 0.300 | position gradient vanishes |
| Pitfall | 0.488 | 0.331 | 0.646 | 0.853 | 0.344 | holds up on position |
| Tennis | 0.477 | 0.954 | 0.000 | 0.406 | 0.350 | position gradient vanishes |
| Name This Game | 0.408 | 0.749 | 0.067 | 0.417 | 0.450 | |
| Ms. Pac-Man | 0.395 | 0.790 | 0.000 | 0.475 | 0.342 | position gradient vanishes |
| Assault | 0.393 | 0.100 | 0.686 | 0.905 | 0.263 | holds up on position |
| Yars' Revenge | 0.378 | 0.756 | 0.000 | 0.464 | 0.250 | position gradient vanishes |
| Double Dunk | 0.364 | 0.709 | 0.018 | 0.808 | 0.583 | position gradient vanishes |
| Seaquest | 0.346 | 0.000 | 0.693 | 0.479 | 0.413 | holds up on position |
| Centipede | 0.339 | 0.678 | 0.000 | 0.450 | 0.472 | position gradient vanishes |
| Berzerk | 0.335 | 0.670 | 0.000 | 0.481 | 0.260 | position gradient vanishes |
| Carnival | 0.330 | 0.661 | 0.000 | 0.474 | 0.403 | position gradient vanishes |
| Chopper Command | 0.318 | 0.000 | 0.636 | 0.452 | 0.400 | holds up on position |
| Hero | 0.291 | 0.091 | 0.491 | 0.417 | 0.265 | |
| Freeway | 0.259 | 0.471 | 0.047 | 0.567 | 0.500 | |
| Venture | 0.243 | 0.487 | 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 rise.jl:131 -> jutari_oracle.jl:174rise_sample runnerrise_importance 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.