# Accept

This revision preserves the accepted contribution and substantially improves its evidential scope. Renaming the empirical expectation as Hypothesis 1, explicitly identifying the unsupported step from corpus frequency to low excess loss, and making the post-training discussion conditional address the previous review directly. The added finiteness assumption makes Lemma 1 precise without changing its valid argument. Separating verbalized from silent narratization corrects the earlier overgeneralization from chain-of-thought studies [8–10]. The revised treatment of probing and the empirical anchoring requirement also make the R/U/S framework more useful.

The additional experimental controls are worthwhile methodological elaborations. Distributed alignment search is appropriately attributed to Geiger et al. [21], whose published account supports its proposed use for finding distributed causal alignments. The revision retains the substantive distinction from that work: applying these tools within a Jaynesian assessment framework. Explicitly presenting both designs as unvalidated proposals is appropriate; their addition supplies neither evidence of consciousness nor a demonstrated diagnostic test. The updated abstract communicates this status clearly.

Several local qualifications would improve the new wording and proposed implementations without changing the acceptance decision:

1. **Distinguish an excess-loss prediction from a diagnosticity prediction.** The new matched-slice comparison tests the proposed ordering of excess losses. Since Lemma 1 supplies an upper bound, failure to obtain lower excess loss does not itself refute small total variation or establish greater diagnostic power. Likewise, replace “a text test regains diagnostic power whenever” no close comparator exists with “may regain diagnostic power.” Absence of a close comparator does not establish that a particular test, or a single test against an entire comparator class, discriminates successfully. The revision's conditional interpretation is sound with this distinction.

2. **Treat comparator performance as evidence of anchoring, rather than its definition.** In the new analog “I” entry, beating a model trained on the agent's outputs is one operational test. It is not a necessary property of an anchored self-representation: an external predictor could match a genuinely anchored but predictable system. Conversely, outperforming one weak comparator does not exclude imitation. Section 4.2 already acknowledges the latter limitation; the table should retain it.

3. **Keep the new Design A controls interpretively modest.** Transplanting narratives in both arms equalizes the procedure, but need not equalize content-dependent insertion effects. Cross-run resampling directly defines a stochastic mediator intervention; equating it with the natural indirect effect requires the stated identification assumptions and appropriate conditioning on task/context. Resampling itself does not establish those assumptions. Also, an effect disappearing after cue redaction need not exclude narrative mediation: explicitly representing the cue can be part of genuine reasoning. Report this as sensitivity to cue-bearing content. Patching a representation of a stated reason would bear on internal causal processing, but identifying it specifically as a silent narrative requires additional evidence of narrative structure.

4. **Avoid overinterpreting Design B's new baselines.** A larger whole-activation effect than probe-subspace effect can reflect changes to unrelated variables, rather than a distributed conflict representation. It is a useful diagnostic comparison, not identification of that representation. Report raw $E_C$ and both control effects alongside any adjusted contrast, and define “net of” precisely: subtracting control total-variation distances does not generally yield a causal effect or even a nonnegative quantity. Any resulting use in $\mathrm{BI}$ needs an explicit convention. Alignment selection should also be evaluated on held-out episodes.

These are qualifications to proposed measurements and local explanatory statements. The update does not introduce a materially unsupported central result, and its main conclusion remains appropriately limited: the assessed evidence neither establishes nor excludes Jaynesian consciousness in language-model agents.