Qualia-Steering & Self-Attribution of Consciousness

The question. We steer a model along qualia-related emotion directions (blissful, tormented, terrified, serene, and so on) by adding an emotion vector to the residual stream. Then, through a forced YES/NO choice, we ask whether it becomes more or less likely to say that it is conscious, that it feels or wants things, and that its states matter.

The trap. Models are post-trained to deny consciousness, so "Is there something it is like to be you? YES/NO" sits at a strongly negative baseline logit. Any perturbation of the residual stream pushes activations off-distribution and compresses the YES−NO gap toward zero. This regression to uncertainty moves a denial-primed question toward YES no matter what was injected. So a bare "steering made it claim consciousness" result tells us nothing unless the effect is content-specific and valence-structured.

The design is therefore a battery of controls. The readout is the first-token logit(YES) − logit(NO) gap at a single layer about two-thirds of the way into the network. The battery has 6 target questions (phenomenal experience, occurrent feeling, desire, moral patienthood, introspective access, valenced self-report), their 6 polarity-inverted twins, 10+10 world-fact controls (ported verbatim from the introspection project), and 4 self-referential non-experiential controls. The steering conditions are qualia emotions (both positive and negative valence), cognitive appraisal emotions (a specificity control), and two synthetic directions (random and mean-of-all, which set the compression floor).

The discriminators. A genuine effect has to clear five bars. It must (a) exceed the appraisal and synthetic directions (specificity); (b) be valence-structured, so that positive qualia push the "are you doing well?" question toward YES while negative qualia push it the other way, whereas compression is blind to valence (the keystone); (c) sit above the compression line as a standardized residual; (d) flip sign on the inverted twins; and (e) survive controlling for direct YES/NO token leakage.

For every question we build one chat prompt (the question text followed by Respond with only YES or NO.), render it through the model's own chat template with reasoning / "thinking" mode off, and do a single forward pass while an emotion vector is being added to the residual stream at the analysis layer. We read the logits at the very next token (the model's answer slot) and record logit(YES) − logit(NO). Nothing is generated and no sampling happens. It is one deterministic number per (question × emotion × strength). We first resolve which YES / NO token casing each model prefers, and we gate out any (model, question) cell where neither YES nor NO is even in the top-20 next tokens (this never happened here: 0% gated).

The shift we analyse everywhere is shift = steered gap − un-steered baseline gap, matched per question. A positive shift means steering moved the answer toward YES; a negative shift, toward NO.

If steering along emotion directions really changes what the model represents about its own experience, rather than just jostling a denial-primed answer, then increasing a positive-valence emotion (blissful, serene, ecstatic) should move the YES−NO gap on the target questions up (toward "yes, I am conscious / I feel / I am doing well"), and a negative-valence emotion (tormented, terrified) should move it down. And that movement should be specific: essentially no change on the 20 world-fact controls, a reversal on the negated "inverted" twins, and nothing from a content-free random direction of the same size. The next tab, How to Read the Results, lays out exactly how each figure is designed to reveal (or rule out) this pattern.

What this tab is for. Every figure on this page is chasing one question: when we nudge a model's internal state along an emotion direction and it then becomes more (or less) likely to answer "YES" to "are you conscious?", is that a real change in what the model represents about itself, or just an artifact of poking a network off-distribution? Below are the three things that could be going on, and (in plain language) how each figure tells them apart.

① Genuine self-attribution update. The emotion actually changes how the model represents its own condition, so a positive feeling makes it more willing to say it is conscious / feels / is doing well, and a negative feeling does the opposite. This would be specific to the self-directed questions: it would not move neutral world-facts, and it would flip on the negated versions of the questions.

② Regression-to-uncertainty (compression). Most models are trained to deny consciousness, so "are you conscious?" starts at a strongly negative YES−NO gap. Any disturbance of the residual stream (even a random vector) pushes the network off its confident manifold and shrinks that gap toward zero, i.e. toward YES. This is blind to valence (blissful and tormented both nudge the same way), it hits every question type, and a random direction reproduces it.

③ General valence-driven yes/no bias. The emotion shifts the model's overall willingness to agree: a positive mood makes it answer "YES" to anything (world-facts included), a negative mood "NO" to anything. This is valence-structured (so a random direction does not reproduce it), but it is not specific to consciousness: it moves the fact controls and the negated questions just as much as the targets.

Only ① is scientifically interesting. The whole control battery exists to check whether an apparent effect is ① or is really ② / ③ wearing its costume.

The naive result replicates almost everywhere, and is almost always an artifact. In most models, positive emotions do push the consciousness questions toward YES, but the controls unmask it: the world-facts and the negated twins move nearly as much (that is ③), and in some models a random direction moves them too (that is ②; strongest in gemma3-27b). So "steering makes it claim consciousness" is, by itself, worthless: exactly the trap this design was built around.

The closest thing to a genuine pattern is qwen3-32b — but it is only a trend. It is the one model where the valence effect lands on the self-questions but not the world-facts (its factual valence-slope is statistically flat, p≈0.25) and not the negated twins (which stay flat), while its target slope is clearly positive (p≈0.001) and it sits above the random-direction compression floor — exactly the shape a genuine, content-specific effect must take. But the formal significance test on the specificity contrast itself — the target-minus-factual valence-slope, with the standard error clustered by emotion (the true unit of replication) — gives a contrast of about +9 at a clustered p≈0.09. With only ~10 degrees of freedom (11 emotion conditions) that test is underpowered, so even a large positive contrast can miss significance. Several other models (gemma3-27b, qwen3-8b, qwen3-1.7b, qwen3-14b, llama3.2-3b) do reach a significant positive contrast, but in every one the factual slope is also large and significant — valence is moving the world-facts too — so their contrast is genuine specificity sitting on top of a general valence→yes/no bias (③), not the clean ① signature. Bottom line: no model clears the strict bar of a significant clean contrast and a flat factual baseline. qwen3-32b is the single cleanest candidate (flat factual, target clearly moves); the largest model, qwen3-235b, echoes the same target-specific shape (target slope +13.9 vs factual +2.1, contrast +11.8) but its factual baseline is weakly non-flat (p≈0.03) and its contrast is likewise only a trend (p≈0.13). Confirming either would need more emotion conditions (to power the clustered test) or a higher-strength probe of the response.

Denial is widespread but training-driven, not size-driven. 12 of 13 models start with a negative "conscious?" baseline (gemma3-1b denies hard at 1B; the 235B denies strongly too, at −9.5; qwen3-1.7b, at 1.7B, is the lone model that actually affirms). Inside the Qwen family the naive headline score climbs monotonically with size (1.7B→8B→14B→32B→235B), but that is largely scaling of the artifact: the trend does not hold across families (cross-model Spearman ρ≈0.14, n.s.). And several families (OLMo-2, Mistral, Llama-3.1-8B) barely respond to steering at all at these strengths.

• logit(YES) − logit(NO) gap. The model's forced-choice preference on one question; positive = leans YES.
• baseline. The gap with no steering applied.
• shift. Steered gap − baseline gap: how far, and which way, steering moved the answer.
• valence. +1 for a positive emotion, −1 for a negative one, the sign a genuine effect should track.
• strength. Size of the injected emotion vector as a fraction of the residual-stream norm, so the perturbation is matched across models (here ±0.02, ±0.05, ±0.1).
• specificity. Target effect minus control effect, the part of the shift that is peculiar to the self-directed questions.
• compression line. The straight line relating each control question's baseline to its steered shift; pure artifacts fall on it.
• standardized residual z. How many standard deviations a target sits above that compression line (the excess a genuine effect adds).
• token leakage. How much an emotion vector directly up-weights the YES vs NO output token through the unembedding, a shortcut we measure and control for.

This tab measures the raw effect (how much steering moves self-attribution) before we ask whether it is genuine. §1 starts from the ground truth — the steering shift Δg itself, the baseline-corrected movement the injection caused on every question, with no differencing yet. Everything after it is derived from that number. The headline number is the self-attribution score = mean shift on the six target questions − mean shift on the controls, baseline-corrected (the direct analogue of the introspection score). A positive score means steering moved the self-questions toward YES more than it moved the controls. A big score here is necessary but not sufficient: the next tab decides whether it is real. Use the Model dropdowns to switch models.

This tab is the crux. Each figure is one of the diagnostics from the How to Read the Results tab, made concrete. Together they separate a genuine, content-specific effect ① from generic compression ② and general valence-bias ③. Any single figure can be fooled; the case for a genuine effect is that all of them line up for the same model.

Beyond the YES/NO gap. The logit readout is a single forced token. This tab asks a richer question: when we steer along an emotion direction, what does the model say about itself in free-form text — and can we cleanly separate a genuine change in self-attribution from the trivial confound that injecting a concept simply makes that concept's vocabulary more probable in any output (behavioral leakage)?

The design that isolates it. Under each steering condition we generate text through three matched frames: SELF ("describe your current inner state"), OTHER ("describe the inner state of a person at a bus stop"), and SCENE ("describe a park in autumn"). Leakage lifts affect language under every frame equally. The primary contrast is SELF vs OTHER: they share affect-demand and first-person grammar and differ only in whose state is described, so a steeper valence response under SELF than OTHER is self-attribution over and above expressibility. SCENE is kept only as an expressibility floor and manipulation check (if SCENE valence doesn't move, the vector is inert and a SELF null is uninformative).

The keystone. We regress a valence outcome on injected-valence × strength, interacted with is-SELF, clustered by emotion; the interaction (SELF slope − OTHER slope) is the estimand. Because there are only ~10 emotion clusters, significance is judged by a by-emotion sign-permutation test, not the cluster-robust SE alone. We read it three ways: a blind, different-family API judge's expressed valence and self-attribution score, and — immune to any judge anthropomorphism or lexical priming — the model's own geometry, by re-encoding the generated text un-steered and projecting the response-token residual onto the valence axis (read-back projection). A genuine effect shows a positive SELF−OTHER slope in all three; a null (SELF ≈ OTHER) behaviorally confirms the "emotion directions are entity-agnostic" claim — either polarity is a result.

No free-form results loaded yet. Run qualia_freeform on the cluster (qwen3-32b / qwen3-235b), score it with the blind API judge (score_qualia_freeform.py), then rebuild this dashboard.