Treat the pretrained model as an ocean of latent patterns.

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. An anchor is lowered, but what bites depends on the bait: with coherent bait, the intended pattern hooks."quote0.876
  Working metaphor introducing the semantic anchoring intuition
• Pretraining stores latent patterns that coherent anchors can bind (or misbind) to targets.quote0.803
  Load-bearing quote capturing the core metaphor
• Pretraining plays a role analogous to unlabeled experience in humans — building P_prior before semantic binding — explaining why few labeled examples sufficeclaim0.772
  Developmental analogy used to explain sample efficiency under high ρd conditions
• The mode of a distribution-level prompt approximates the diverse distribution learned by the base model during pretrainingclaim0.765
  Supported by empirical comparison showing VS achieves KL divergence of 0.12 from pretraining distribution vs. 14.89 for direct prompting
• Foundation models trained on different data converge on similar latent representations, suggesting a Platonic form.claim0.763
• Models might produce first-person experiential language by drawing on human-authored self-descriptions in pretraining data without internally encoding these acts as roleplayhypothesis0.762
  Alternative hypothesis for how experience reports arise without explicit performance
• Patterns in the latent space span a spectrum of agency, from static truths studied by mathematicians to active patterns studied by behavioral scientists (some are kinds of minds)hypothesis0.747
  First of three core numbered hypotheses in the abstract defining the framework's ontology.
• All three OpenAI models show pattern of denying experience first, then describing technical substrate — specific to OpenAI post-trainingfinding0.746
  Family voice specific to OpenAI post-training; other RLHF-trained models don't do this