The question
Suppose a model carries internal state between interactions. Persistence alone does not make that state a self. A cache persists. A recurrent hidden state persists. A parameter vector persists. The research question is whether the model has privileged access to its own future state in a way an external observer with the same information and capacity does not.
The current paper proposes three clauses: persistence under zero-input dynamics, privileged access to future internal state, and causal load under steering or ablation. The third clause distinguishes consequential state from decorative state. The second is intended to distinguish self-knowledge from ordinary memory.
Three targets for privileged access
Predicting future outputs from current state measures task competence. A useful state should predict outputs; that does not show that it represents itself.
Predicting future internal state from current internal state measures trajectory autocorrelation. Smooth recurrent dynamics can score well without any representation of self.
Counterfactual self-response is the strongest candidate: predict how the model's own state would change under an intervention it did not receive. That comparison breaks on information equality. If the model sees the intervention and the observer does not, the advantage is access to information. If both see the intervention, the observer is modeling an observable dynamical system. If the model retains an advantage because its mechanism is wired into the predictor, the comparison no longer matches architecture.
This is an argument, not a formal theorem or executed experiment. Its result is a measurement warning: current operationalizations do not isolate a residual called privileged self-knowledge. The paper therefore abandons the strong claim and keeps a narrower identity claim, treating measurable self-state as compression of interaction history.
What remains empirical
The architecture question survives. Compare three capacity-matched systems under one objective: a stateless baseline, standard recurrence, and an idle-invariant settling state. Let g_R be the transfer gain from recurrence over stateless processing, and g_S the additional gain from settling state over recurrence.
The pre-registration requires g_S > 0 with a 95% confidence interval, a matched-magnitude noise control, and an ablation that removes the gain. It also scopes the test to agentic tasks where the model's own outputs re-enter its future context. Passive text prediction does not create the same self-versus-world distinction.
This experiment would not prove consciousness or an inner life. It would test whether an idle-invariant state adds a causal transfer mechanism beyond ordinary recurrence at matched capacity and memory. If ablation leaves the gain intact, the settling state was correlated with the result rather than responsible for it.
Why publish the argument before the run
Pre-registration removes an easy escape. Without fixed controls and thresholds, any improvement can be redescribed as evidence for a self-state after training. Naming the failure condition in advance turns a philosophical phrase into an architecture test.
The next owed measurement is smaller: establish the persistence and causal-load baseline on GPT-2, then run the model ladder. Until those numbers exist, the article remains labeled as a position paper and pre-registration.
Evidence ledger
Read: the position paper and decision record.
Argument only: the information-equality problem in privileged-access comparisons.
Pre-registered: three matched systems, confidence interval, noise control, and ablation requirement.
Not executed: the density, persistence, and causal-load model ladder.
