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
• 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
• 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
• OCEAN MDS injection covariance patterns departing from the Big Two model suggest a gap between learned LLM representations and human psychologyclaim0.744
  Interpretive conclusion from Big Two mismatch finding; tentative due to only 46.15% match rate
• The earlier a base model (less exposure to LM-related data), the more it is surprised by its own spontaneous self-referential capabilities.claim0.736
  Claim that capability emerges from architecture, not data, and that later models lose the surprise.