Asynchronous Update Training

Training regime where random subsets of cells update per step, improving robustness of learned circuits

Neighborhood — ranked by edge-count

framework

Differentiable Logic Cellular Automata (DiffLogic CA)
uses
The novel framework introduced in this paper, combining DLGN and NCA for fully differentiable discrete CA learning

concept

Asynchronous Updates
implements
Update strategy where random subsets of cells are updated per step, simulating independent cell clocks

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Models trained directly with asynchronous updates would exhibit even greater robustness than synchronously trained modelshypothesis0.788
Hypothesis that motivated the asynchronous robustness comparison experiment
asynchronous communicationconcept0.775
Communication where sender does not block; dominant in Linda.
Synchronous Updatesconcept0.770
Default update strategy where all cells update simultaneously; contrasted with asynchronous updates
Asynchronous training improves robustness of DiffLogic CA circuits to perturbations compared to synchronous trainingclaim0.749
Interpretive claim supported by damage resilience experiment results
Harness-Updating Capabilityconcept0.721
The capability of an evolver model to produce useful persistent harness updates from execution evidence
Associative Learningconcept0.711
Sentience criterion; capacity occurs even in gene regulatory networks and non-neural morphogenetic agents.
Synchronously trained DiffLogic CA circuit succeeds at asynchronous inference without retrainingfinding0.704
Unexpected result demonstrating robustness of learned circuits beyond their training regime
Update Stageconcept0.704
Second stage of DiffLogic CA where a DLGN computes each cell's new binary state from perception output and current state