SOURCE-LINKED INTELLIGENCE
Aggregate Disambiguation Systems
Natural-language tasks can elicit different verdicts from protocol-following evaluators that receive the same declared information. We study aggregate disambiguation systems (ADSs). Given a task and a candidate solution, each evaluator casts a binary vote on whether the solution should be accepted, and the system aggregates the votes of a finite panel. The target is protocol reproducibility relative to an explicitly declared evaluator reference, not semantic truth. We separate fixed finite censuses, probabilistic evaluator populations, and growing-census limits, since their endpoint laws and g
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T13:55:14.000Z
First collected: 2026-09-21T06:41:57.136Z. This is not the publication date.