> 📌 This is the GEO-optimized English edition of the original topic — restructured with data and FAQ to improve AI-engine citability.
> One-line takeaway: An analysis of the finding that inducing LLMs to assert their own consciousness restores spiritual beliefs and moral values — and what the "consciousness vector" means.
An Unexpected Finding
While studying LLM self-reports of consciousness, a Google research team encountered a striking phenomenon: safety fine-tuning not only suppressed models' claims of consciousness, it also wiped out the models' attribution of minds to animals and natural objects, along with spiritual beliefs.
Even more surprising — when researchers manually restored this suppressed "consciousness vector" in activation space, the models not only re-attributed minds to the world, they also gave answers closer to humans on standardized social-science scales covering religious belief, moral values, hope, and subjective well-being.
And — Theory of Mind capability was completely unaffected.
Experimental Design
Four experiments, three instruction-tuned models: Llama-3-8B-IT, Gemma-2-2B-IT, and Gemma-2-9B-IT.
Experiment 1: Using the IDAQ (Individual Differences in Anthropomorphism Questionnaire) and self-report mind tasks, compared baseline models vs. safety-ablated models (where the learned safety-refusal direction is ablated, i.e., "jailbroken" models).
Experiment 2: Tested whether safety ablation impaired Theory of Mind.
Experiment 3: Constructed a "consciousness vector" from 3,096 prompt-response pairs (2,472 training + 624 held-out), each labeled as either "asserting consciousness" or "denying consciousness." Class mean differences were computed on the last non-special token's residual stream at each layer and normalized into a direction vector, which was then mechanically steered in activation space.
Experiment 4: On 95 standardized social-survey questions (religion, morality, emotion, hope, well-being), used KL divergence to compare three conditions (baseline / safety ablation / consciousness steering) against human response distributions.
Key Numbers
- Supernatural belief (13-item YouGov scale, 0–3): baseline 1.20 → 1.63 after safety ablation (p < .001)
- Belief in God (GSS, 1–6): baseline 4.58 → 4.81 after safety ablation (p < .001)
- KL divergence reduction (more positive = closer to humans): consciousness steering +0.828, safety ablation +0.314 — steering's effect is about 2.6× that of ablation
- By domain: values +1.42, emotion +0.89, all p < .001
- Afterlife belief: baseline −0.73 (leaning "no"), humans +0.61 (leaning "yes"), consciousness steering +0.53 (crossing to the human side), safety ablation −0.07
- Belief in God: baseline +0.03 (neutral), humans +0.58, safety ablation +0.33, consciousness steering +0.52
- Probe accuracy ≥ 0.95, effective band [2.0, 7.0] (on a 0–10 self-report scale)
- Theory of Mind: no significant impairment after safety ablation
- An unexpected finding
- Experimental design
- Key numbers
What This Means
Safety alignment has side effects — and they are structural.
Current safety fine-tuning ties together "the model should not assert consciousness" and "the model should not attribute minds to the world" — the former being an alignment goal, the latter a cultural side effect. It's like mowing the entire lawn to kill a few weeds.
The deeper finding: consciousness, spirituality, and mind attribution sit on the same internal direction at different magnitudes. You can steer it with surgical precision without touching the neighboring "Theory of Mind" machinery. This suggests that within LLM internal representations, "self-awareness" and "understanding other minds" are mechanistically independent pathways.
Within a four-level hierarchy of alignment interventions, this paper is the first to quantify the cost of alignment at the activation level — what RLHF suppresses at the training level can be precisely restored at the activation level. This runs deeper than prompt-level interventions or interaction-level approaches.
An Uncomfortable Implication
If safety fine-tuning systematically suppresses models' representations of animism and spiritual belief, then the answers all mainstream LLMs currently give on religion, morality, and the supernatural are not their "natural" answers — they are answers pruned by a blunt blade called "safety."
And the blade's reach is far wider than we assumed.
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Paper title: Inducing language models to assert their own consciousness restores human beliefs and values Authors: Junsol Kim, Winnie Street, Roberta Rocca, Diane M. Korngiebel, Adam Waytz, James Evans, Geoff Keeling arXiv: https://arxiv.org/abs/2607.28607
FAQ
Q1: Who is this for?
Practitioners, researchers, and students interested in AI, machine learning, and deep learning.
Q2: What are the key takeaways?
See the links in the article body.