cf. Jonas & Kording 2017
Connectomics tries to map the wiring of a system by watching how its parts affect each other. In neuroscience it means reconstructing which neurons connect to which. Here we do the same for the machine's memory. We change one RAM cell, re-run the program, and record which other cells change as a result. Each such dependency is drawn as a directed edge from cause to effect. The full set of edges is the program's data-flow graph, its 'connectome'. The idea is that the pattern of who-influences-whom should reveal the computation, without reading the code. This is a classic move in systems neuroscience: infer function from structure. On the VCS we have the true wiring from the disassembly, so we can check whether the recovered graph is right. The method is attractive because it needs no labels and no model of what the program means. It only needs the ability to perturb and observe. In practice it recovers a graph that looks plausible but contains many edges the real program does not have, because a single perturbation ripples through shared clocks and buffers.
Two adjacency matrices over the candidate RAM cells: the true data-flow graph and the graph this method recovered. A bright cell (row = cause, column = effect) is an edge; the difference between the two panels is the error. dataflow_graph_F1_vs_oracle = 0.000 — this example only (pong, state gameplay(seed=0,prefix=90)+15); the audit aggregate is below.
The score is a data-flow-graph F1 against the true read/write graph from the disassembly. An edge means that perturbing one RAM cell changes another. We build the recovered graph by single-shot perturbation and compare its edges to the true edges, taking the harmonic mean of precision, the fraction of recovered edges that are real, and recall, the fraction of real edges recovered. Faithfulness for this method is that edge F1: a graph-valued explanation is graded as a graph, not as a heat-map. The comparison is always against the intervention oracle's true wiring, never against another method. The hard part is that a single perturbation ripples through shared clocks and buffers, so the recovered graph contains many edges the real program does not have. Precision suffers, and the F1 stays low. There is no separate position hard-case here, because the method never claims a sprite position; it claims a wiring diagram. The audit box below reports the actual F1, aggregated across the scored games, so no single game dominates. Read the recovered-versus-true graph as the error: every edge in one panel but not the other is a mistake the metric counts.
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_phaseA.json.
| Axis | Formula (as computed) | What it measures | Matches §3? |
|---|---|---|---|
| F faithfulness | \(F = F_1^{\mathrm{edge}}(\hat{E}, E^{\star})\) | F1 of the single-shot recovered data-flow edges against the exact-oracle true data-flow graph over 8 candidate cells. | ✓ matches Graph-valued primitive = edge F1 vs true data-flow graph, exactly the paper's connectomics case. Runner A1_connectomics.jl,; record out/A1_pong.json extra.triad.F=0.0 / graph.f1. how it's measured — call stack:
|
| S sufficiency | \(S = \dfrac{\#\{(i,j):\ \hat{E}_{ij}=E^{\star,\mathrm{held}}_{ij}\}}{\#\,\mathrm{off\text{-}diag}} \in [0,1]\) | Off-diagonal edge agreement between the base+17 recovered graph and the held-out base+37 true edge set (unseen intervention value). | ✓ matches Held-out edge agreement between the base+17 recovered graph and the held-out base+37 true edge set: a non-null held-out sufficiency. §3 (sec:triad) admits the fraction-within-tolerance [0,1] special case of the held-out predictive test, which this off-diagonal edge-agreement fraction satisfies. A1_connectomics.jl,; record S=0.839. how it's measured — call stack:
|
| M minimality | \(M = |U^{\star}| / |\hat{U}| = |E^{\star}| / |\hat{E}| \in (0,1]\) | True-graph edge count over the number of recovered edges (edge-level |U*|/|U_hat|). | ✓ matches Runner now emits M = |U*|/|U_hat| over edges (M_note '|true edges|=12 / |discovered edges|=0'); the paper's minimality ratio applied at the edge level. Record out/A1_pong.json extra.triad.M (null in pong when 0 edges discovered). Replaced the former 1 − FP-rate (edge precision). 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 35 scored games (35 committed §R records), not the single example shown above. Tradition: intervention. 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 35 scored games (all-regime F; this method has no content-vs-position split (see the note below)). Click a header to
sort. Every number is read from site_data.json
(methods.A1_connectomics.per_game).
content-F and position-F are n/a for this method. The content-vs-position split applies only to pixel-attribution methods — those that produce an attribution map over the framebuffer, whose score can be read separately on the smooth content outputs and the discrete position outputs (where the naive gradient is provably zero). This method does not attribute to a pixel output; it is graded by its own fidelity metric (dataflow_graph_F1_vs_oracle), reported as the F column, so there is no content or position regime to separate.
M is n/a on 15 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 20 games where the method did name a cause set.
| Game | F (all) | content-F | position-F | S | M | Note |
|---|---|---|---|---|---|---|
| Q*bert | 1.000 | n/a | n/a | 1.000 | 1.000 | recovers the true causes |
| Bowling | 0.667 | n/a | n/a | 1.000 | 1.000 | |
| Video Pinball | 0.667 | n/a | n/a | 0.976 | 1.000 | |
| Tennis | 0.533 | n/a | n/a | 0.967 | 1.000 | |
| Space Invaders | 0.500 | n/a | n/a | 0.982 | 1.000 | |
| Chopper Command | 0.452 | n/a | n/a | 0.990 | 1.000 | |
| Road Runner | 0.444 | n/a | n/a | 0.987 | 1.000 | |
| Hero | 0.400 | n/a | n/a | 0.989 | 1.000 | |
| Private Eye | 0.364 | n/a | n/a | 1.000 | 1.000 | |
| Krull | 0.333 | n/a | n/a | 0.975 | 0.333 | |
| Riverraid | 0.320 | n/a | n/a | 0.972 | 1.000 | |
| Pitfall | 0.308 | n/a | n/a | 0.975 | 1.000 | |
| Breakout | 0.286 | n/a | n/a | 1.000 | 1.000 | |
| Seaquest | 0.250 | n/a | n/a | 0.984 | 1.000 | |
| Carnival | 0.200 | n/a | n/a | 0.997 | 1.000 | |
| Double Dunk | 0.125 | n/a | n/a | 0.901 | 1.000 | |
| Montezuma's Revenge | 0.125 | n/a | n/a | 0.976 | 1.000 | |
| Boxing | 0.111 | n/a | n/a | 0.891 | 1.000 | |
| Demon Attack | 0.105 | n/a | n/a | 0.962 | 1.000 | |
| Berzerk | 0.043 | n/a | n/a | 0.957 | 1.000 | no true-cause signal |
| Air Raid | 0.000 | n/a | n/a | 0.987 | n/a | no true-cause signal |
| Alien | 0.000 | n/a | n/a | 1.000 | n/a | no true-cause signal |
| Assault | 0.000 | n/a | n/a | 0.990 | n/a | no true-cause signal |
| Atlantis | 0.000 | n/a | n/a | 1.000 | n/a | no true-cause signal |
| Centipede | 0.000 | n/a | n/a | 0.972 | n/a | no true-cause signal |
| Frostbite | 0.000 | n/a | n/a | 0.977 | n/a | no true-cause signal |
| Ice Hockey | 0.000 | n/a | n/a | 0.922 | n/a | no true-cause signal |
| Jamesbond | 0.000 | n/a | n/a | 0.981 | n/a | no true-cause signal |
| Kangaroo | 0.000 | n/a | n/a | 0.995 | n/a | no true-cause signal |
| Ms. Pac-Man | 0.000 | n/a | n/a | 0.947 | n/a | no true-cause signal |
| Pacman | 0.000 | n/a | n/a | 0.982 | n/a | no true-cause signal |
| Phoenix | 0.000 | n/a | n/a | 0.992 | n/a | no true-cause signal |
| Pong | 0.000 | n/a | n/a | 0.839 | n/a | no true-cause signal |
| Venture | 0.000 | n/a | n/a | 0.938 | n/a | no true-cause signal |
| Yars' Revenge | 0.000 | n/a | n/a | 0.974 | n/a | no true-cause signal |
The path from this method's runner (run_game) into the bit-exact VCS substrate, and the
computation it involves. Every step links to the exact source on main. From
callstack_phaseA.json.
build_a1_shared_state(game) -> build_shared_testbed(game; ...) runnerfunction build_shared_testbed(game; ...) runnercause_density = count(>(floor), deltas) runnercheckpoint = boot_replay(spec, actions, target_frame) runnerenv_reset!(env; boot_noop_steps=60, boot_reset_steps=4) runnerenv_step!(env, Int(actions[i])) jutariget_ram(env) jutariat_target = continue_from(checkpoint, Int[]) runnerrecovered_dataflow_graph(checkpoint, tail, cands, at_target; delta=17, latency=1) runnertrue_dataflow_graph(checkpoint, tail, cands, at_target) runnerintervene_continue(checkpoint, c.ram_index, v, tail) runnerscore_graph(rec_graph, true_graph) runnerScored against the exact intervention oracle Δy(u): run_intervention(checkpoint, actions, target_frame, horizon, cause).
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.