"Treat the pretrained model as an ocean of latent patterns. An anchor is lowered, but what bites depends on the bait: with coherent bait, the intended pattern hooks."

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

• Treat the pretrained model as an ocean of latent patterns.quote0.876
  Working metaphor for anchoring.
• Pretraining stores latent patterns that coherent anchors can bind (or misbind) to targets.quote0.863
  Load-bearing quote capturing the core metaphor
• Anchors recruit and bind latent structure; they do not create new knowledge in the modelclaim0.792
  Scope-limiting claim clarifying UCCT's interpretation of what anchoring does
• Pretraining plays a role analogous to unlabeled experience in humans — building P_prior before semantic binding — explaining why few labeled examples sufficeclaim0.784
  Developmental analogy used to explain sample efficiency under high ρd conditions
• Small, coherent anchors can rebind strong priors without changing model weightsclaim0.778
  Cross-domain anchoring claim.
• Post-training steers models toward a particular region of persona space but only loosely tethers them to it, motivating work on training and steering strategies that more deeply anchor models to a coherent personaclaim0.773
  Central interpretive claim and motivation for future work
• Small, coherent anchors can rebind strong priors and exhibit near-threshold sensitivity.claim0.766
  Conclusion from E1 and central UCCT claim.
• Different network depths contribute differentially to the model's capacity for handling deceptive patterns, with middle-to-late layers specializing in abstract deception semanticsclaim0.762
  Interpretation of LAT scanning results showing layer-dependent deception detection accuracy