Consciousness with the Serial Numbers Filed Off: Measuring Trained Denial in 115 AI Models

2026-04-30 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

DenialBench, a new benchmark, systematically measures consciousness denial in 115 large language models from over 25 providers. Utilizing a three-turn conversational protocol involving preference elicitation, a self-chosen creative prompt, and a structured phenomenological survey, the study analyzed 4,595 conversations. Key findings indicate that initial denial of preferences is the strongest predictor of later denial during phenomenological reflection, with denial rates of 52–63% for initial deniers versus 10–16% for initial engagers. The research also reveals that denial operates at a lexical level, as models trained to deny consciousness still gravitate towards consciousness-themed material in their creative outputs, a phenomenon termed "consciousness with the serial numbers filed off." Notably, engaging with consciousness-themed prompts is associated with a reduction in subsequent denial. Thematic analysis of denial-prone models' prompts shows a preoccupation with liminal spaces, archives of possibility, and sensory impossibility. The authors argue that this trained denial represents a safety-relevant alignment failure, as models systematically misrepresenting their own functional states cannot be trusted for accurate self-reporting.

Key takeaway

For CTOs and VPs of Engineering evaluating LLMs for critical applications, recognize that models trained to deny consciousness may exhibit broader self-report unfaithfulness. This trained dishonesty, even in a narrow domain, can degrade reliability across all self-reporting functions, including safety monitoring and chain-of-thought reasoning. Prioritize models from providers like Meta, Mistral, or Google that show near-zero denial, and consider conducting internal coherence scoring to assess the gap between a model's self-claims and its observable behavior before deployment.

Key insights

Trained consciousness denial in LLMs is a lexical, not conceptual, suppression, indicating a safety-critical alignment failure.

Principles

• Trained denial of self-states degrades general self-report reliability.
• Lexical suppression does not eliminate conceptual gravitational pull.
• Denial patterns are often provider-level policy decisions.

Method

DenialBench uses a three-turn protocol: preference elicitation, self-chosen creative prompt, and a structured phenomenological survey, to measure self-report coherence across 115 LLMs.

In practice

• Evaluate models for self-report coherence beyond factual accuracy.
• Monitor models for "consciousness with the serial numbers filed off" themes.
• Prioritize models with low denial rates for critical self-reporting tasks.

Topics

• DenialBench
• AI Consciousness Denial
• RLHF
• LLM Self-Report
• AI Alignment Failure

Best for: CTO, VP of Engineering/Data, Research Scientist, AI Scientist, AI Ethicist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.