Non-closing truth recursion prompts destabilize LLM attention matrices with large effect sizes, unlike grounded self-reference or factual controls, and increase contradictory model outputs.
interpreting GPT : the logit lens
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When Self-Reference Fails to Close: Matrix-Level Dynamics in Large Language Models
Non-closing truth recursion prompts destabilize LLM attention matrices with large effect sizes, unlike grounded self-reference or factual controls, and increase contradictory model outputs.