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.
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2026 2verdicts
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Sparse autoencoders show OOD prompts increase fallacious concept activation in transformers, offering a mechanistic measure of shift and a path to robust fine-tuning.
citing papers explorer
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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.
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At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization
Sparse autoencoders show OOD prompts increase fallacious concept activation in transformers, offering a mechanistic measure of shift and a path to robust fine-tuning.