A reproducibility and provenance dashboard for the two papers built on the differentiable Atari 2600 VCS. Every claim links to the exact code, command, artifact, runtime and hardware — and to the external reference that proves the emulator is real.
Learning explainable AI? The XAI primer and the 30 method guides teach each method on neural networks and then run it, with code, on a machine whose true answer we know.
Space Invaders — xitari (reference C++) · jutari (our Julia port) · pixel difference. The difference panel is solid black: byte-for-byte identical output.
Validated bit-for-bit against an independent C++ emulator
The strongest defence against “the AI hallucinated it” is that the two differentiable ports are checked against xitari — a separately written, pre-existing C++ Atari 2600 emulator that is not part of this project. On all 64 ALE-supported games the ports reproduce xitari’s 128 B of RAM byte-for-byte and its 210×160 framebuffer pixel-for-pixel. Hallucinated code does not accidentally match an external reference to the bit.
main with the prompt→code chain; a 7,525-line running bug-fix log — bug_fix_log.mdThis site covers Paper 1 and Paper 2 only.
Two end-to-end differentiable ports of the xitari VCS, validated bit-for-bit
Open the evidence ledger →
Nature Machine Intelligence (in preparation)Scoring XAI / mechanistic-interpretability methods against the exact causal truth of a known machine
Open the evidence ledger →
Each row in a paper’s ledger is one auditable result.
| Field | What it gives the reviewer |
|---|---|
| Value | the measured number, read from the committed result file |
| Script | link to the exact source that produces it |
| Command | the verbatim invocation to reproduce it |
| Artifact | the committed output (results table / JSON / figure) it wrote |
| Runtime & Hardware | wall-clock and the machine it ran on |
| Verified by | the test / oracle gate that guards it |
| Status | measured vs deferred — we never dress a plan up as a result |