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semantic-satiation

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JYS1025 / gestaltzerfall-llm

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Computational phenomenology study of semantic satiation in neural networks. Comparing how GPT-2, BERT, and Mamba handle extreme repetition reveals causal models drift into hallucination while bidirectional models stay stable—suggesting attention directionality preserves semantic identity.

transformer computational-linguistics mamba bert ai-safety state-space-model hallucination cognitive-modeling gpt-2 mechanistic-interpretability semantic-satiation mamba-ssm gestaltzerfall
  • Updated Jan 18, 2026
  • Python

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