构建可微分的雅达利游戏系统，为可解释人工智能提供真实可验证的测试基准。
A Differentiable Atari VCS:A Complex, Fully Known Ground Truth for Explainable AI
- 将雅达利2600主机重实现为两个完全可微的模拟器，支持梯度计算。
- 在64个游戏上实现字节级内存和像素级画面完全一致，验证了精确性。
- 适合研究基于梯度的可解释AI方法，尤其关注复杂系统中的解释可信度。
解释需要真实基准：要验证一个系统的解释，必须了解其内部机制——而这正是可解释人工智能（XAI）最需要却最缺乏的。现有系统分为两类：简单但机制透明的模型（如决策树）无法检验解释能力；复杂的真实系统（如深度网络）虽需解释，却无内核真值，导致解释可能看似合理却错误难辨。本文通过重构真实的雅达利2600视频计算机系统（VCS），构建了一个既真正复杂又完全已知的系统：使用Julia（jutari）和JAX（jaxtari）分别实现两个端到端可微的模拟器，经比特级验证与xitari一致。两者在64个支持的ALE游戏中均实现64/64字节级内存和64/64像素级屏幕输出。将卡带ROM视为权重张量、内存视为软磁带、控制流视为门，证明在任意有限温度下前向传播中可微执行等价于原生硬逻辑执行，并在无梯度处暴露代理梯度。JAX版本支持GPU加速，单块消费级显卡每秒可达百万级环境步数。系统开发耗时约137小时，部分由编程智能体自主完成。本文从理论与定性梯度分析两方面证明基于梯度的XAI在此系统中可行。两个版本代码均已开源（MIT许可）于https://github.com/akmaier/UnderstandingVCS。
原文摘要 · Abstract (English)
Explanation requires ground truth: to verify an account of a system we must know its inner functioning-just what is missing where explainable AI (XAI) is most needed. Systems we can study fall into two camps. Simple, procedural one-decision trees, rule lists, sparse linear models-have a known but trivial mechanism, so explaining them tests nothing; genuinely complex ones-deep networks, real-world tasks-need XAI but have no ground-truth inner functioning, so an explanation can be plausible, confident, and wrong with no way to tell. We remove this dichotomy with a study object both genuinely complex and fully specified-inspectable by construction-and, so gradient methods apply, fully differentiable. We reimplement the Atari 2600 Video Computer System (VCS)-a real computer architecture, and the cradle of deep reinforcement learning-as two independent end-to-end differentiable emulators in Julia (jutari) and JAX (jaxtari), each validated bit-for-bit against xitari. Both reproduce xitari on all 64 supported Arcade Learning Environment (ALE) games: 64/64 byte-identical RAM and 64/64 pixel-identical screens. Treating the cartridge ROM as a weight tensor, RAM as a soft tape, and control flow as gates, we prove the differentiable (soft) execution equals the original (hard) one bit-for-bit in the forward pass at any finite temperature, while exposing surrogate gradients where the bit logic has none. The JAX port also opens a GPU path: batched differentiable rollouts reach millions of environment-steps/s on one commodity GPU. The system was built in roughly 137 active hours over 29 calendar days, much of it written autonomously by coding agents. This paper builds and validates the foundation, showing-theoretically and in a qualitative gradient study-that gradient-based XAI on it is feasible. Both ports' full code is available under the MIT license at https://github.com/akmaier/UnderstandingVCS.
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