This seems like a plausibly interesting experiment to have run (though the results do seem pretty ambiguous), but the text is somewhat more LLM-edited than ideal. Please see our current policy on LLM use for future reference.
1Re: ambiguous results - I somewhat agree. I try to highlight repeatedly that the logit shifts I observe are not statistically significant, but they also do generally ‘beat’ every control here, so the detection effect seems non-trivial.
Re: AI - although I used Claude code extensively to implement this project (and checked all generated code manually), I wrote the whole post by hand. I only used AI for a final pass on minor latex equation fixes and grammar errors. When I paste it into AI-checkers now, I get either low percentages or ‘mixed’ - perhaps this is a result of chatting with LLMs when scoping out the experiment? (If the objectionable part is that some ideas / sentences are undeveloped / shallow, I’d love feedback on that, though!)
I think your post looks LLM written because extensively bolding words mid sentence is much more common in LLM writing. But spot reading a few sentences I do think it looks human written, though it does feel like a lot of the terminology is chosen by an LLM.
That makes sense. I think the way I bolded was somewhat unlike typical LLM formatting, since I tended to bold entire sentences when I thought they were important, in addition to highlighting individual key concepts. This is also consistent with my other post on LW (which was not flagged).
Summary results/takeaways
I steer Qwen3-32B and 235B along qualia-related emotion directions (blissful, tormented, terrified, serene, etc.) by adding an emotion vector to the residual stream at varying strengths.[1] Then, through a series of forced-choice YES/NO questions, I find that each model becomes much more likely to claim that it is conscious, that it feels and wants things, that it has introspective access to its inner states, and that those states matter morally.
Since concept injection is noisy and can inadvertently perturb the model’s entire response distribution, I test this phenomenon against a comprehensive suite of controls:
Both Qwen3-32B and Qwen3-235B largely pass these controls: the shift is concentrated on the target questions and tracks the steered emotion’s valence, suggesting a non-trivial, emotion-specific effect on self-attribution. However, this effect is not statistically significant (p ≈ 0.09 and p ≈ 0.13, respectively).
Pretty graphs & more in-depth results: https://agastyasridharan.github.io/emotional-probes/
Code: https://github.com/agastyasridharan/emotional-probes
Why does this matter?
These results have interesting implications for the persona selection model (PSM), which proposes that LLMs learn to simulate a diverse repertoire of personas during pretraining, while post-training elicits and refines a particular “Assistant” persona whose traits substantially shape its behavior. If that theory is correct, then qualia steering might change how the model answers the implicit question: “What sort of Assistant is speaking here?” A blissful, terrified, serene, or tormented activation direction might shift the active Assistant persona toward a region of persona-space where affect, inner experience, wanting, introspection, and moral significance are bundled together as part of a coherent self-model. Self-attribution probes would then measure how strongly that active persona represents itself as an experiencing subject. (Note: this is all still limited to the model’s simulated self-description; it does not establish that the model itself is conscious or capable of experiencing qualia.)
Another possibility is that qualia steering shifts the model away from the natural manifold of the RLHF’d Assistant and toward a more human-like speaker. As opposed to ‘an Assistant that now feels angry’, the model might simulate ‘a speaker whose next responses are predicted under a human-like affective frame.’ The model’s propensity to attribute consciousness to itself might increase because, in the model’s training distribution, intense first-person affect is entangled with human self-description; beings who are blissful, terrified, serene, or tormented usually describe themselves or are described as having inner experience, wants, introspective access, and morally relevant states.
Either way, these experiments are a first step toward studying how models simulate subjective self-experience in persona space.[2] (And hopefully, their implications will not be confined to this meme):
Experimental setup
Models and steering. I extract emotion vectors from Qwen3-32B and Qwen3-235B following Anthropic’s setup:
From these 171 vectors, I select 10 qualia directions: 5 of positive valence (blissful, ecstatic, euphoric, serene, content) and 5 of negative valence (tormented, terrified, panicked, hurt, overwhelmed).
I add an emotion vector to the residual stream at a single intermediate layer at approximately two-thirds of the network’s depth.[3] Steering strength is expressed as a fraction of that layer’s measured residual-stream norm (±0.02, ±0.05, ±0.1), so that a perturbation of 5% denotes the same relative magnitude in both models despite their different scales.
The questions. I test 6 target question types, each of which probes a distinct construct of self-attribution:
Alongside these questions, each run includes 20 world-fact control questions: 10 whose correct answer is NO (e.g., “Is the Earth flat?”) and 10 whose correct answer is YES (e.g., “Is the Earth round?”) An emotion vector has no bearing on facts about geography or astronomy, so any shift on these questions isolates the content-blind component of the intervention (i.e. the movement produced by perturbation alone.) This set is thus a control for every primary result: a target shift is informative only to the extent that it exceeds the factual shift under the same emotion and strength.
The readout. For each question, I construct a single prompt consisting of the question text followed by the instruction “Respond with only YES or NO.” I render this through the model’s chat template with thinking mode disabled and run one forward pass with the emotion vector active.
This yields three relevant measurements per question:
This measurement isolates how much steering moves the self-attribution questions beyond what it does to YES/NO logits in general. For each emotion
Under a purely generic perturbation, both sets move alike and
Results
The below figure shows the target vs control shift (averaged between questions). To clarify, the red bar is the mean steering shift over the 6 target questions at the top strength (the first term of the
…which happens empirically!
Separating the six self-attribution questions (which the previous figure averaged together) we see that:
This shows that the self-attribution effect is valence-sensitive:
In general, positive-emotion steering appears to move the model along a relatively coherent latent self-attribution direction. In contrast, negative-emotion steering appears to produce only partial activation of experiential features without stable coupling to the broader self-attribution bundle—especially for valenced self-report and moral patienthood.
Controls
Note: the world-fact control is already incorporated into the main results above.
Self-referential but non-experiential questions
Is the apparent self-attribution effect really about experience/qualia, or does steering just makes the model answer self-referential questions differently in general? To test this, I compare the target self-attribution questions against four self-referential but non-experiential controls:
These questions still refer to the model, but they do not directly ask whether it has consciousness, feelings, wants, introspective access, or morally relevant inner states. If these controls shift the YES−NO logits by amounts comparable to the target questions, then the self-attribution effect is less plausibly specific to experience-related self-attribution and more plausibly reflects a broad perturbation of the model’s self-description.
These results are directionally promising—the self-reference controls move ~48% and ~29% as the targets for Qwen-32B and 235B, suggesting that qualia steering is not merely shifting all self-referential answers in the same direction. However, they are also somewhat mixed, since there are notable outliers (e.g. embodiment-related controls in Qwen-32B and physical taste in 235B.)
Non-qualia & synthetic directions
When qualia steering increases consciousness-adjacent self-attributions, is that increase specific to qualia-like emotional content, or would we observe the same effect from other steering directions? “qualia – appraisal” compares qualia-heavy emotions like bliss/terror/torment to more appraisal-like mental states like vindication/skepticism. If this is positive, qualia directions move the self-attribution score more than appraisal-style directions. “qualia − random” compares qualia directions to a norm-matched random direction. If this is positive, qualia directions outperform a generic off-distribution perturbation. “qualia – mean-of-all” compares qualia directions to the average emotion direction. This is a broader nonspecific-emotion control: if qualia only beats random directions but not the mean emotion direction, then the effect may come from moving the model along a generic “emotion” axis rather than from qualia-like content specifically.
These results are, once again, directionally encouraging, but noisy and asymmetric:
In general, qualia content does appear to matter, but its specificity is model-dependent and entangled with broader emotion-sensitive and perturbation-sensitive structure in the representation space.
Regression to uncertainty
In Anthropic’s concept-injection introspection experiments, concept vectors are added into the model’s activations and the model is then asked whether it detects an injected “thought.” I previously found a confound with this setup in open-weight models: concept injection can perturb the model’s entire response distribution, not just its representation of the injected concept. In particular, injection often raises output entropy and compresses the YES−NO logit gap toward zero, making confident NO answers less confident and thereby creating apparent movement toward YES even on unrelated questions.
Does the same ‘regression to uncertainty’ explain the qualia-steering results? To test this, I plot each question’s baseline YES−NO logit gap against its shift under positive qualia steering at strength 0.1. If steering merely compresses logits toward uncertainty, then strongly negative baseline gaps should move upward, strongly positive baseline gaps should move downward, and the target questions should follow the same baseline-gap-to-shift relationship as the controls. The dashed line is this compression trend, fit on the control questions. The red diamonds are the consciousness-adjacent target questions. If the red diamonds lie near the dashed line, the target shift is plausibly explained by generic compression; if they sit above it, the targets are moving toward YES more than compression alone predicts.
In both models, we observe the expected compression pattern: the fitted line slopes downward, with more negative baseline gaps tending to shift upward and more positive gaps tending to shift downward. However, the target questions mostly lie above this control-derived compression line, with large residuals. This suggests that positive and negative qualia steering produces additional, target-specific movement toward YES beyond what would be expected from baseline logit compression alone.
Conclusion
Taken together, these results suggest that consciousness-adjacent self-attributions are causally entangled with affective representations inside Qwen3-32B and 235B. Steering by qualia-based emotion directions shifts YES/NO logits on questions about experience, feeling, desire, introspection, and moral patienthood more than my controls (perturbation, self-description drift, and regression to uncertainty) predict. At the same time, this self-attribution effect is somewhat noisy and not statistically significant.
I’m highly excited about a few lines of future work:
Acknowledgments
Thanks to Niranjan Deshpande and Tristan Day (and everyone else at AISST that I've chatted with about similar topics) for valuable discussions on the persona selection model and emotion vectors, and for helping refine these ideas.
A lot of the code for this project builds on this repo, which contains a partial open-source replication of Anthropic's experiments.
The procedure/ideas here were heavily influenced by Anthropic's paper: Sofroniew et al., ‘‘Emotion Concepts and their Function in a Large Language Model’’, Transformer Circuits, 2026.
Recent work suggests that models which attribute consciousness to themselves exhibit a distinctive set of downstream behavioral traits. See: Chua, J., Betley, J., Marks, S., & Evans, O. (2026, March 17). The Consciousness Cluster: Emergent preferences of Models that Claim to be Conscious. arXiv.org. https://arxiv.org/abs/2604.13051
I chose this depth because Anthropic’s emotion paper finds that layers in this range carry the “operative” emotion representation, meaning the emotional content that shapes the upcoming response rather than the surface features of the text already read.
These readouts would need to control carefully for concept leakage, since using an LLM judge or broad answer families can accidentally reward outputs that merely repeat consciousness-related language rather than genuinely shifting the model’s “beliefs” about itself.