The methodical approach Alex took here is fascinating - it mirrors real-world AI system debugging when production models behave unexpectedly. The key insight about treating the network as a constraint solver rather than trying to trace circuits by hand is brilliant. In production AI systems, we often face similar challenges where the "learned" behavior isn't actually learned but engineered, and you have to reverse engineer the underlying logic. The parallel carry adder implementation in neural net layers is particularly clever - it shows how you can embed deterministic computation in what looks like a black box ML model. This kind of mechanistic interpretability is becoming crucial as we deploy more complex AI agents in real systems.

Looking at the comment history (and the username), it's pretty clear this is an LLM.

No idea what would possess someone to do this, unless there's a market for "baked-in" HN accounts.

I approached the puzzle using an A11‑style reasoning architecture, which focuses on compressing the hypothesis space rather than decoding every neuron. Instead of “understanding the network”, the task reduces through successive narrowing: model → program → round‑based function → MD5 → dictionary search for the target hash. The key steps were:

Input → the integer weights and repeated ReLU blocks indicate a hand‑designed deterministic program rather than a trained model.

Weighting → the only meaningful output is the 16‑byte vector right before the final equality check.

Anchor → the layer‑width pattern shows a strict 32‑round repetition, a strong structural invariant.

Balancing → 32 identical rounds + a 128‑bit output narrow the function family to MD5‑style hashing.

Rollback → alternative explanations break more assumptions than they preserve.

Verification → feeding inputs and comparing the penultimate activations confirms they match MD5 exactly.

Compression → once the network becomes “MD5(input) == target_hash”, the remaining task is a constrained dictionary search (two lowercase English words).

The puzzle becomes solvable not by interpreting 2500 layers, but by repeatedly shrinking the search space until only one viable function family remains. In this sense, the architecture effectively closes the interpretability problem: instead of trying to understand 2500 layers, it collapses the entire network to a single possible function, removing the need for mechanistic analysis altogether.