Paper 2 · per-game results

Bowling

The exact ground truth for this ROM and how every interpretability method scores against it — one of the 42 scored games. Every number is read from site_data.json (games.bowling).

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Bowling screen

Ground truth

External label source
AtariARI + OCAtari
Verified causal labels
8
Labels that move a sprite
7
Scorable (enough true causes)
yes
Position regime
yes — a moving tracked sprite at this frame

The screenshot is the exact analysis frame the ground truth is computed on. Labels are imported (AtariARI / OCAtari) then verified causally on the bit-exact machine.

Every method's faithfulness on Bowling

Each XAI / mechanistic-interpretability method's faithfulness on this ROM, sorted by F (click a header to re-sort). The left border marks the tradition: green = causal / intervention, red = gradient / correlational, amber = other — the split the paper's headline turns on. F is all-regime; content-F and position-F are the two output regimes (n/a where this game has no record for that regime).

MethodFamilyF (all)content-Fposition-FSM
A2 · Single-unit lesionscausal / intervention1.000n/an/a0.6641.000
A8 · Whole-state recordingdescriptive1.000n/an/a1.0000.062
Activation patching / causal mediationcausal / intervention1.000n/a1.0001.0000.955
Causal scrubbingcausal / intervention1.000n/an/a1.0001.000
Interchange interventions / DAScausal / intervention1.000n/an/a1.0000.909
Logit / tuned lenscausal / intervention0.995n/an/an/a0.298
Occlusioncausal / intervention0.8970.9330.8610.8811.000
LIMEgradient / correlational0.8730.9040.8430.8810.225
KernelSHAP / Shapleygradient / correlational0.8020.7670.8380.8810.225
Path patching / IOI circuitcausal / intervention0.800n/an/an/a1.000
RISEgradient / correlational0.7900.7740.8060.8570.225
A1 · Connectomics / data-flow graphcausal / intervention0.667n/an/a1.0001.000
ACDC — automatic circuit discoverycausal / intervention0.667n/an/a0.0501.000
On-distribution counterfactualcausal / intervention0.6610.8960.4250.8811.000
Extremal / meaningful perturbationcausal / intervention0.6370.7600.5130.8811.000
Grad×Input / DeepLIFTgradient / correlational0.6280.9080.3480.9051.000
A4 · Pairwise correlationsgradient / correlational0.609n/an/a0.5270.667
A3 · Tuning curvesgradient / correlational0.603n/an/a0.5731.000
Sparse autoencodersdim_reduction0.584n/an/a0.4761.000
Expected Gradientsgradient / correlational0.5180.6880.3480.9051.000
Integrated Gradientsgradient / correlational0.5180.6880.3480.9051.000
Vanilla gradient (saliency)gradient / correlational0.5180.6880.3480.9051.000
SmoothGradgradient / correlational0.5180.6880.3480.9051.000
A7 · Dimensionality reduction (NMF/PCA)dim_reduction0.400n/an/an/an/a
Guided Backpropgradient / correlational0.3440.6880.0000.8811.000
A5 · Local field potentialsgradient / correlational0.298n/an/an/an/a
A6 · Granger causalitygradient / correlational0.286n/an/a0.9921.000
Attribution / edge patchinggradient / correlational0.000n/an/a0.9451.000
NMF/PCA dictionariesdim_reduction0.000n/an/a0.8001.000

Source: site_data.json · generated by gen_site_data.py from the committed §R records + leaderboard.json.