If we abandon emotion labels entirely and instead learn safety-relevant hidden-state clusters purely from behavioral co-occurrence patterns (e.g., joint profiles over refusals, de-escalation style, calibration, verbosity), do the resulting unsupervised “behavioral state” directions recapitulate existing functional emotion vectors, split them into multiple distinct mechanisms, or cut across them in ways that reveal important failure modes currently hidden by the emotion framing—for example, a cluster that combines high prosocial language with systematically overconfident, under-cautious advice?

Answer

The most realistic expectation is that unsupervised “behavioral state” directions will partially overlap with known functional emotion vectors but also frequently split and cut across them, and that some of these crossings will expose safety-relevant failure modes—such as prosocial tone combined with overconfident, under-cautious advice—that are obscured by emotion labels alone.

Concretely:

• Some clusters will recapitulate coarse functional emotions (e.g., a state with high refusals, careful hedging, and soft de-escalation that aligns with a “concern/caution” emotion bundle).
• Many others will split emotion vectors into mechanisms (e.g., separating “warm prosocial style” from “risk-aversion” and from “epistemic humility”), revealing that one nominal emotion contained multiple partly independent safety levers.
• A non-trivial subset will cut across emotion vectors, yielding mixed states like “high prosocial language + low risk-aversion + overconfidence,” which are not cleanly captured by any single functional emotion and are especially important for safety.

So abandoning labels and clustering on behavioral co-occurrence is likely to enrich and refine the functional-emotion picture rather than fully replace it: it will show where emotion vectors are useful mid-level summaries and where they hide safety-relevant structure and failure modes.