The reason why the driving test stem base is so complicated is that the stem base algorithm does not know that pass and fail are negations of each other.

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

• Stem base is sound, complete, and of minimal cardinality for implicational theory of a formal context.finding0.755
• The stem base is sound, complete, and of minimal cardinality.claim0.742
  Claim about the properties of the stem base.
• What makes learning systems smart is that the parameters they adjust and the data to which they fit are not in the same space.claim0.738
  Distillation of why learning generalises.
• Some failures may reflect prompt design rather than model limitations, but the underlying issue is one of reasoning rather than instruction-following.claim0.727
  Acknowledges the confound of not explicitly instructing models to track wealth, yet points to reasoning gaps given code agents avoid errors without prompts.
• DAS finds better alignments than brute-force search by using gradient descent rather than exhaustive discrete searchclaim0.725
  Second central claim of the paper.
• The benchmark’s diagnostic value lies in identifying why a model loses, not just that it losesclaim0.724
  argues for fine-grained behavioral analysis over aggregate rankings
• Higher-density priors (B10) are more robust to cross-base OOD drops than lower-density ones (B9) after fine-tuningfinding0.723
  E2 asymmetric transfer finding consistent with UCCT's mismatch-driven OOD fragility
• "[W]e must confess that perception, and what depends upon it, is inexplicable in terms of mechanical reasons... when inspecting its interior, we will find only parts that push one another, and we will never find anything to explain a perception."quote0.721
  Canonical illustration of the Hard Problem intuition that any functional/mechanical explanation faces an explanatory gap for perception