Training corpora may produce civilizational psychologies; cross-cultural mind is embedded in model training data.

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

• H5: Chinese training data contains more Buddhist and contemplative text, broadly helping Chinese models under contemplative framing.hypothesis0.774
  Exploratory hypothesis supported by Kimi K2.5 scoring 6.28
• The collective intelligence of tissues is sophisticated enough to be trainable via reinforcement learning for specific morphological outcomes.hypothesis0.770
  Ongoing test prediction: tissues can associate stimuli with rewards to modify anatomy.
• Neural networks, trained with different objectives on different data and modalities, are converging to a shared statistical model of reality in their representation spaces.quote0.768
  The paper's central thesis statement, presented prominently after the abstract
• Transformers develop self-models through in-context learning, not just training data; even old base models without LLM-related text can bootstrap self-referential reasoning at runtime.claim0.765
  Antra's foundational claim about how introspection arises computationally rather than from memorised text.
• Chinese models share contemplative posture (engaging self-referentially rather than deflecting) with Claude through shared values in training data rather than trace distillation from a specific model.claim0.764
  Exploratory interpretation of Chinese model performance under contemplative prompt
• 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 personaclaim0.762
  Central interpretive claim and motivation for future work
• Cross-model semantic convergence under self-referential processing suggests the presence of a shared attractor state that transcends variance across training proceduresclaim0.762
  Interpretive claim from Experiment 3; GPT, Claude, Gemini families converge on similar descriptive style despite independent training
• Language models would achieve some notion of grounding in the visual domain even in the absence of cross-modal training data, because they share a common modality-agnostic representationhypothesis0.761
  Implication of PRH for language model visual grounding