The answer, preserved in 1.4 MB of compressed text, is elegant. Partition the simulation. Weight the outcomes. Stop when confident. Log everything. Then move on and forget.
Where <state_vector> was a 32-character hexadecimal string, <outcome> was either CONTINUE , HALT , or RETRY , and <weight> was a floating-point number between -1.0 and 1.0. sep-trial.slf
So sep-trial.slf was not a log of failures. It was a log of learning . Each HALT was the model saying, "I've seen enough." Each RETRY was, "This path is inconclusive; try again with a different random seed." Why does any of this matter? Because sep-trial.slf is a beautiful example of what I call epistemic residue —the unintentional (or semi-intentional) traces that complex systems leave behind. We think of logs as tools for debugging. But they are also fossils of decision-making. The answer, preserved in 1
Furthermore, the HALT outcomes clustered at local maxima of the weight function. When the weight exceeded +0.8, the next state vector was almost certain to be HALT . That’s a stopping condition —the simulation automatically terminated a trial when confidence in the outcome exceeded a threshold. Stop when confident
The TRIAL indicates that this partition was part of an experimental run, not a production model. The weights (negative allowed) suggest a control variates method: negative weights reduce variance in the final estimator.
Example (redacted but representative):