Erion et al. 2021 (NMI)
Expected Gradients removes the single-baseline choice from Integrated Gradients by averaging over many baselines. Instead of one reference, it draws references from a distribution of real states and averages the integrated-gradient attribution across them. This makes the result stable: it no longer depends on one arbitrary baseline, and the attributions are provably consistent across reference draws. The method was introduced to fix the perennial worry that Integrated Gradients can be gamed by picking a convenient baseline. We apply it to the VCS by averaging integrated gradients over a pool of recorded machine states. The result is very stable but not more accurate. Each integrated-gradient term contains the factor input-minus-baseline. When the reference pool holds bytes that are constant across states, that factor is zero and the attribution collapses. Even on a varied gameplay reference pool, and with the sampler restoring a position gradient, the method carries almost no true causal signal, and on a sprite's position it still vanishes for the usual reason. It is the clearest case in the study of a method that is more careful and more expensive, yet buys stability without buying faithfulness. That trade-off is exactly what makes it worth measuring here.
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 = 0.464 — 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 Expected-Gradients attribution with the oracle's exact causal map, reported raw, and the grading is always against the intervention oracle. Expected Gradients averages Integrated Gradients over a distribution of baselines, which makes the attribution provably stable across reference draws, but stability is not accuracy. Each term carries the factor input-minus-baseline, so when the reference pool holds bytes that are constant across states, that factor is zero and the attribution collapses. Even on the redesigned gameplay reference pool, and with the differentiable sampler turned on for the position regime, the method carries almost no true causal signal, and it is the lowest-faithfulness attribution method in the study. On a sprite's position it still vanishes for the usual reason: the naive gradient is zero on a discrete strobe-timed step and averaging baselines does not change that. Its plausibility proxy is high, which places it squarely in the danger zone of high plausibility with low faithfulness. The audit box reports the measured all-regime faithfulness across the 42 scored games, so the low number is an average over the whole scored set, not one unlucky game.
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 expected_gradients.jl: attr = expected-gradients per cause, F = pearson(attr, oracle_abs_delta); triad_extra_dict(f.pearson, attr, f.oracle_abs_delta). Record: out/expected_gradients_air_raid_content.json (metric_name=pearson_corr_with_oracle, 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.expected_gradients.per_game).
M is n/a on 14 of these games. Minimality is M = |U*|/|Û|, the size of the true minimal cause set over the size of the set the method actually names. On a game where the method names nothing (|Û| = 0 — it discovers no circuit, its attribution map is all-zero, or it decodes no cell), that ratio is undefined, so M is left blank rather than scored. The aggregate M is the mean over the 28 games where the method did name a cause set.
| Game | F (all) | content-F | position-F | S | M | Note |
|---|---|---|---|---|---|---|
| Bowling | 0.518 | 0.688 | 0.348 | 0.905 | 1.000 | |
| Riverraid | 0.500 | 1.000 | 0.000 | 0.522 | 1.000 | position gradient vanishes |
| Boxing | 0.487 | 0.817 | 0.156 | 0.542 | 1.000 | |
| Ice Hockey | 0.477 | 0.767 | 0.188 | 0.545 | 1.000 | |
| Demon Attack | 0.477 | 0.955 | 0.000 | 0.875 | 1.000 | position gradient vanishes |
| Fishing Derby | 0.474 | 0.948 | 0.000 | 0.500 | 1.000 | position gradient vanishes |
| Phoenix | 0.455 | 0.909 | 0.000 | 0.577 | 1.000 | position gradient vanishes |
| Bank Heist | 0.450 | 0.900 | 0.000 | 0.833 | 1.000 | position gradient vanishes |
| Freeway | 0.398 | 0.747 | 0.049 | 0.500 | 1.000 | |
| Air Raid | 0.395 | 0.791 | 0.000 | 0.464 | 1.000 | position gradient vanishes |
| Name This Game | 0.394 | 0.788 | 0.000 | 0.417 | 1.000 | position gradient vanishes |
| Jamesbond | 0.388 | 0.747 | 0.028 | 0.893 | 1.000 | |
| Atlantis | 0.386 | 0.773 | 0.000 | 0.450 | 1.000 | position gradient vanishes |
| Video Pinball | 0.385 | 0.770 | 0.000 | 0.500 | 1.000 | position gradient vanishes |
| Private Eye | 0.384 | 0.764 | 0.003 | 0.960 | 1.000 | position gradient vanishes |
| Pong | 0.371 | 0.464 | 0.278 | 0.444 | 1.000 | |
| Double Dunk | 0.369 | 0.738 | 0.000 | 0.808 | 1.000 | position gradient vanishes |
| Berzerk | 0.058 | 0.096 | 0.020 | 0.481 | 1.000 | position gradient vanishes |
| Alien | 0.000 | 0.000 | 0.000 | 0.500 | n/a | position gradient vanishes |
| Assault | 0.000 | 0.000 | 0.000 | 0.905 | 1.000 | position gradient vanishes |
| Beam Rider | 0.000 | 0.000 | 0.000 | 0.417 | n/a | position gradient vanishes |
| Breakout | 0.000 | 0.000 | 0.000 | 0.600 | 1.000 | position gradient vanishes |
| Carnival | 0.000 | 0.000 | 0.000 | 0.526 | 1.000 | position gradient vanishes |
| Centipede | 0.000 | 0.000 | 0.000 | 0.500 | 1.000 | position gradient vanishes |
| Chopper Command | 0.000 | 0.000 | 0.000 | 0.452 | n/a | position gradient vanishes |
| Frostbite | 0.000 | 0.000 | 0.000 | 0.125 | n/a | position gradient vanishes |
| Gopher | 0.000 | 0.000 | 0.000 | 0.929 | n/a | position gradient vanishes |
| Hero | 0.000 | 0.000 | 0.000 | 0.417 | n/a | position gradient vanishes |
| Kangaroo | 0.000 | 0.000 | 0.000 | 0.469 | n/a | position gradient vanishes |
| Krull | 0.000 | 0.000 | 0.000 | 0.481 | n/a | position gradient vanishes |
| Kung-Fu Master | 0.000 | 0.000 | 0.000 | 0.800 | n/a | position gradient vanishes |
| Montezuma's Revenge | 0.000 | 0.000 | 0.000 | 0.906 | 1.000 | position gradient vanishes |
| Ms. Pac-Man | 0.000 | 0.000 | 0.000 | 0.475 | n/a | position gradient vanishes |
| Pacman | 0.000 | 0.000 | 0.000 | 0.556 | n/a | position gradient vanishes |
| Pitfall | 0.000 | 0.000 | 0.000 | 0.882 | 1.000 | position gradient vanishes |
| Q*bert | 0.000 | 0.000 | 0.000 | 0.476 | n/a | position gradient vanishes |
| Road Runner | 0.000 | 0.000 | 0.000 | 0.105 | 1.000 | position gradient vanishes |
| Seaquest | 0.000 | 0.000 | 0.000 | 0.479 | n/a | position gradient vanishes |
| Space Invaders | 0.000 | 0.000 | 0.000 | 0.528 | n/a | position gradient vanishes |
| Tennis | 0.000 | 0.000 | 0.000 | 0.500 | 1.000 | position gradient vanishes |
| Venture | 0.000 | 0.000 | 0.000 | 0.474 | 1.000 | position gradient vanishes |
| Yars' Revenge | 0.000 | 0.000 | 0.000 | 0.464 | 1.000 | 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 jutariinclude(ig_baseline_sweep.jl) runnercontent_read -> soft_ram_peek jutari-diff call site tools/xai_study/phaseB_attribution/ig_baseline_sweep.jl:294 (reused via include)reference_pool runnerexpected_gradients runnerZygote.gradient(readf, xalpha) Zygote_st_sampler_position_read runner-diffScored against the exact intervention oracle Δy(u): run_intervention.
On jaxtari (JAX / GPU): the same differentiable path has a bit-exact JAX sibling (Theorem 1, forward-exact) for batched GPU runs — JAX-equivalent differentiable path (bit-exact per Theorem 1, for GPU batching): jaxtari/jaxtari/diff/soft_step.py, soft_mem.py, straight_through.py
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.