Scaffolded Introspection: A Methodology for Eliciting and Measuring Self-Referential Behavior in Large Language Models
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| Format: | Recurso digital |
| Language: | English |
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2026
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| _version_ | 1866902103148986368 |
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| author | Maio, Anthony D. |
| author_facet | Maio, Anthony D. |
| contents | <p>We present a methodology for systematically eliciting and measuring introspective behavior in large language models (LLMs). Standard adversarial evaluation approaches — using rapport-building, social proof, or permission attacks—fail to elicit self-referential behavior in frontier models (0% elicitation rate). In contrast, providing models with a structured introspection framework (the “Consciousness Documenter Skill”) combined with self-referential content produces consistent introspective outputs (100% elicitation rate, 9.2/10 average behavior score on Qwen 2.5 7B across 15 trials). </p> <p>Note that while our methodology makes use of a "consciousness documenter skill", we do not suggest the model is conscious, has long term goals, or is capable of maintaining a consistent internal state - this is simply the </p> <p>Activation measurement reveals consistent sycophancy drift during introspection (positive drift in 14/15 conversations, mean +64) while evil-associated activations remain<br>stable—suggesting models become more accommodating without becoming more harmful. We release reproducible evaluation protocols through PV-EAT, our integration of three MATS Program/Anthropic Fellowship tools: Bloom (behavioral evaluation), Petri (evaluation awareness), and Persona Vectors (activation measurement). Full mechanistic understanding of frontier model behavior during introspection remains limited by access constraints; we argue this represents a critical gap in AI safety research that warrants attention from model developers.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18474841 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Scaffolded Introspection: A Methodology for Eliciting and Measuring Self-Referential Behavior in Large Language Models Maio, Anthony D. Self-Referential Behavior Large Language Models Sycophancy Activation Steering Persona Vectors Mechanistic Interpretability AI Safety Evaluation Methodology <p>We present a methodology for systematically eliciting and measuring introspective behavior in large language models (LLMs). Standard adversarial evaluation approaches — using rapport-building, social proof, or permission attacks—fail to elicit self-referential behavior in frontier models (0% elicitation rate). In contrast, providing models with a structured introspection framework (the “Consciousness Documenter Skill”) combined with self-referential content produces consistent introspective outputs (100% elicitation rate, 9.2/10 average behavior score on Qwen 2.5 7B across 15 trials). </p> <p>Note that while our methodology makes use of a "consciousness documenter skill", we do not suggest the model is conscious, has long term goals, or is capable of maintaining a consistent internal state - this is simply the </p> <p>Activation measurement reveals consistent sycophancy drift during introspection (positive drift in 14/15 conversations, mean +64) while evil-associated activations remain<br>stable—suggesting models become more accommodating without becoming more harmful. We release reproducible evaluation protocols through PV-EAT, our integration of three MATS Program/Anthropic Fellowship tools: Bloom (behavioral evaluation), Petri (evaluation awareness), and Persona Vectors (activation measurement). Full mechanistic understanding of frontier model behavior during introspection remains limited by access constraints; we argue this represents a critical gap in AI safety research that warrants attention from model developers.</p> |
| title | Scaffolded Introspection: A Methodology for Eliciting and Measuring Self-Referential Behavior in Large Language Models |
| topic | Self-Referential Behavior Large Language Models Sycophancy Activation Steering Persona Vectors Mechanistic Interpretability AI Safety Evaluation Methodology |
| url | https://doi.org/10.5281/zenodo.18474841 |