Agent-drafted interpretation, pending human review

This collection does what the entangled source stage needs most: it produces a uniformly strong task solver without collapsing mixture quality uniformly across the board.

The strongest evidence is the separation between the saturated answer metric and the task-dependent structure metrics. All six tasks finish at essentially perfect sampled-test answer accuracy, but unique-strategy rate ranges from 0.6839 on grid_pathfinding to 1.0 on linear_equation_solving, base_conversion, and multidigit_addition, while final probe mean ranges from 0.6993 to 0.9659. That is exactly the kind of source-model heterogeneity later disentangling collections can test against.

The cleanest source checkpoints appear to be multidigit_addition and base_conversion. Both retain full unique-strategy behavior at the reported precision and have the strongest probe signal in the collection. list_summation is also clean behaviorally, but its probe signal is meaningfully weaker. linear_equation_solving is the opposite pattern: fully unique at the output level, but only moderately decodable internally. grid_pathfinding and sorting_algorithms are the most entangled in the behavioral sense because ambiguity remains visible at convergence.

The specific ambiguity pattern is informative. grid_pathfinding still spreads mass between named strategies and a large ambiguous bucket, while sorting_algorithms zeroes out heap-sort and selection-sort entirely and concentrates on three strategies plus ambiguity. That suggests the source model is not simply mixing all available strategies uniformly; it is learning task-specific mixture geometry with selective support.

Agent-drafted interpretation, pending human review

As a project artifact, this collection looks like a successful source-model report rather than a final benchmark. It shows that the pretrained LoRA entangled baseline can solve the full task suite while preserving enough strategy structure to make later factorization experiments meaningful.

That matters for the downstream comparison line in two ways. First, it gives synthetic-pretrained-disentangled-from-entangled-v1 a concrete upstream source whose strengths and weaknesses are already characterized. Second, it creates a more grounded contrast with ss-disentangled-direct-pretrained-v1, where the core question is whether latent factorization can recover cleaner structure than this source model exposes directly.

Agent-drafted interpretation, pending human review

The main operational caveat is not model quality but artifact history: base_conversion has two abandoned retry directories. The aggregate pipeline handles this correctly by selecting the complete final attempt and reporting the abandoned attempts only in the status table, but the collection should still be described as 6/6 complete with 2 abandoned retries rather than as a perfectly clean directory tree.

The second caveat is that this report is source-only. It intentionally does not claim that the highest-probe tasks are automatically the easiest disentangling cases, only that they are the cleanest entangled checkpoints by the metrics available at this stage.