aggregate_categorical(): pure function over a per-(identity, primitive)
observation list. Five-state vocabulary, last-N=5 window comparison
with one-outlier-tolerant majority threshold:
* unknown — < 3 observations
* stable — recent 5 agree (≥ 4 of 5 share top value), older 5 same
* drifting — recent 5 stable but disagrees with older 5, or older
was conflicted and recent stabilised
* conflicted — recent 5 split, no two-value alternation pattern
* multi_actor — recent 5 split + alternation between exactly two
values (operator A↔B handoff). Confidence capped at 0.6 per
_thresholds.MULTI_ACTOR_MAX_CONFIDENCE; flapping primitives on
flaky networks would otherwise look like two operators.
aggregate_observations() dispatcher honours value_kind="categorical"
(or None) and raises NotImplementedError for "numeric" / "hash" so
Phase 3 lands cleanly. 14 synthetic-input tests cover every state
+ boundary condition.
anti
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