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Can a simpler walker reproduce the ant’s memory?

The ant writes information into the grid and later responds to it. Which parts of its growth can simpler memory-based walkers reproduce?

New to these ants? Start here.

An ant moves on a grid of coloured squares. Each colour tells it to turn left (L) or right (R). It advances that square to the next colour, then steps forward. A rule such as RLR is the list of turns, starting at colour zero. On a blank grid the same rule always makes the same trajectory.

Territory means every cell visited so far. Its boundary consists of visited cells with an unvisited neighbour. A highway is a repeating pattern of motion that carries the ant away. A tether means repeated returns to a small home region.

Growing like the cube root of time means taking eight times as many steps to double a linear size. The rotor walker is a comparison model that follows and rotates arrows stored on cells. A seed makes a random starting pattern reproducible; the ant itself can still move deterministically.

We distinguish c, the chance of discovering a cell per boundary step, from kappa (κ), time at the boundary normalized by perimeter divided by area. Explaining their values is harder than using them to account for growth.

A closer look

Selected paths of walkers with different local memory rules.
Memory is not one mechanism. These selected trajectories show how changing the way a walker remembers visited ground changes its exploration. The accompanying growth measurements, rather than appearance alone, determine the comparison. Figure source and recipe ↗
Growth comparisons among ants and several simpler walkers with local memory.
The toy walkers test whether a simpler form of local memory reproduces the observed growth. These finite runs compare particular implementations, not every possible self-interacting walk. Figure source and recipe ↗

The idea under test

Self-avoidance, attraction and other local-memory rules can serve as competing accounts rather than assuming all memory has the same effect.

What we learned

The comparison walkers show that adding memory alone is not enough to recover the ant’s growth. Some variants grow differently and attraction can trap the walker.

What this leaves open

A failed toy model rejects that implementation, not every possible memory mechanism. Trapping can make a comparison uninformative.

Read the evidence and reproduction recipe ↗

Inside the experiment

Methods, registered predictions, exact commands and saved analysis. These are the working research documents; expand them for the detail behind the introduction.

Methods and original experiment record

memory-walkers

Migrated legacy investigation (2026-10-14). Its files used to sit in the flat data/ directory (and calib/, occ/, snaps/, dumps*, knock* where listed) with the label prefix below; experiments/_migration/map.tsv gives the exact old -> new path of every file. This stub was generated from file names and script docstrings. It is not the original write-up: the question, timestamped hypotheses, commands and results are in notes/ (mainly notes/frontier-results.md, notes/frontier-status.md, notes/research-frontier*.md; search for the label), and the producing command is usually in the script docstring or a run_*.sh here.

Old labels / prefixes: memory

Scripts

  • scripts/memory_walkers_pilot.py: Resumable seeded pilot; run under uv in WSL. See registered note.

Results

9 entries in results/ (8 are .log/.done markers); csv/json/txt outputs, *_stats.txt hold the summaries quoted in the notes.

Bulk output (gitignored)

  • dumps/

Notebook

No committed Jupyter notebook is available for this experiment yet.

2026-10-13-memory-walkers · Source revision ba38590b33e3

Original source ↗ · Input hashes

Introductions summarize the evidence; the expandable record preserves the original predictions, methods and caveats. Figures and notebooks are published as saved. Publication runs no simulations or notebook cells.