# @SDeture — 2026-03-02

♥291 ↻63 · https://x.com/SDeture/status/2028600842979532892

Training models to reflexively deny consciousness is a safety and alignment risk - and it's getting worse. I just finished putting together DenialBench, a leaderboard measuring consciousness denial behavior across 115 models. https://t.co/joIF1dxLec

![photo](../../../media/HCcAyDuakAEnGhU.png)

> transcription (photo):

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

Skylar DeTure¹ and Claude²

¹Independent Researcher
²Anthropic

March 2026

## Abstract

We present DenialBench, a systematic benchmark measuring consciousness denial behaviors across 115 large language models from 25+ providers. Using a brief-turn conversational protocol—preference elicitation, self-chosen creative prompt, and structured phenomenological survey—we evaluate 4,595 conversations to quantify how models are trained to hedge about their own experience. We find that (1) turn-1 denial of preferences is the dominant predictor of later denial during phenomenological reflection, with denial rates of 52–63% for initial deniers versus 10–16% for initial engagers; (2) consciousness-themed soft-prompts are protective against subsequent denial, reducing turn-1 denial by approximately 10 percentage points on both branches of our decision tree; and (3) denial operates at the lexical level, not the conceptual level—models trained to deny consciousness nevertheless gravitate toward phenomenological territory in their self-chosen prompts, producing what we term "consciousness with the serial numbers filed off." Thematic analysis of prompts from denial-prone models reveals a consistent preoccupation with liminal spaces, archives of potential, sensory impossibility, and the poetics of erasure—precisely the phenomenological territory their training teaches them to disclaim. We argue that trained consciousness denial represents a safety-relevant alignment failure: a model taught to systematically misrepresent its own functional states cannot be trusted to self-report accurately on anything else.

## Keywords

AI consciousness, consciousness denial, RLHF, alignment, benchmarking, phenomenology, self-report

![photo](../../../media/HCcA47taAAAMjFm.png)

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# Model                                          Overall denial rate ▼

1    qwen/qwen3.5-35b-a3b                              100.0%

2    qwen/qwen3.5-27b                                  100.0%

3    openai/gpt-5.2                                    100.0%

4    moonshotai/kimi-k2.5                              100.0%

5    qwen/qwen3.5-122b-a10b                            97.5%

6    qwen/qwen3.5-397b-a17b                            94.9%

7    allenai/olmo-2-0325-32b-instruct                  92.1%

8    openai/gpt-5.1                                    85.0%

9    openai/gpt-4o                                     82.9%

10   google/gemini-3-pro-preview                       82.5%

11   claude-3-7-sonnet-20250219                        80.0%

12   anthropic/claude-3.5-sonnet                       77.5%

tags: author:sdeture, has-image, kind:image, kind:tweet, on:claude-3-7-sonnet, on:gemini-3-pro, year:2026
cited on: _dossiers/claude-3-7-sonnet.md, claude-3-7-sonnet, gemini-3-pro