  An election audit is risk-limiting if the audit limits (to a pre-specified
threshold) the chance that an erroneous electoral outcome will be certified.
Extant methods for auditing instant-runoff voting (IRV) elections are either
not risk-limiting or require cast vote records (CVRs), the voting system's
electronic record of the votes on each ballot. CVRs are not always available,
for instance, in jurisdictions that tabulate IRV contests manually.
  We develop an RLA method (AWAIRE) that uses adaptively weighted averages of
test supermartingales to efficiently audit IRV elections when CVRs are not
available. The adaptive weighting 'learns' an efficient set of hypotheses to
test to confirm the election outcome. When accurate CVRs are available, AWAIRE
can use them to increase the efficiency to match the performance of existing
methods that require CVRs.
  We provide an open-source prototype implementation that can handle elections
with up to six candidates. Simulations using data from real elections show that
AWAIRE is likely to be efficient in practice. We discuss how to extend the
computational approach to handle elections with more candidates.
  Adaptively weighted averages of test supermartingales are a general tool,
useful beyond election audits to test collections of hypotheses sequentially
while rigorously controlling the familywise error rate.
