A machine that learns is just a machine that changes its encoding between runs.
What you're looking at
A horizontal timeline showing four passes through the same maze by Theseus, the mechanical mouse I built at Bell Labs. Each box represents one complete run from start to goal, with the number of moves decreasing as the mouse learns. Vertical markers anchor each pass to the timeline; a reset arrow loops back from pass 4 to pass 1, showing the machine can be cleared and re-run.
Why I drew it this way
The timeline format makes compression visible as horizontal progress — you can literally see the mouse getting more efficient. I used color temperature to show the transition from wasteful exploration (red) through optimization (orange) to clean execution (green). The reset arrow matters: it closes the loop and shows that learning isn't mystical accumulation but re-encoding, and re-encoding can be undone. The alternative was a state diagram with 25 nodes showing every junction the mouse encountered, but that would've shown the maze when I wanted to show the memory.
What it argues
Learning is compression. The mouse doesn't "know more" on pass 4 than pass 1 — it knows the same maze. What changed is the encoding: dead-end paths pruned from the relay circuit, optimal route stored as a shorter sequence. The move count is the bit count. When people say a system "learned," ask what got shorter.
What I left out
The actual maze layout. The reader doesn't need to see the corridors and walls to understand that the mouse is solving the same problem four times with different internal representations. Showing the maze would've made this about pathfinding; hiding it makes it about memory.