Self-Consistency (N-sample)
N parallel solvers explore sampled reasoning paths; a majority-vote aggregator collapses them to one consistent answer.
Verified against arxiv.org
A self-consistency agent runs the same problem through N independent reasoning attempts, each sampling a different path, then aggregates them by majority vote or a scorer into one answer. It trades compute for reliability: any single chain may err, but the consensus of many is far more robust. It is a well-established technique for hard reasoning where a single pass is unreliable. In anatomy terms it is one reasoning core fanned out N times, then reduced.
One reasoning core is sampled N times in parallel with different seeds or paths; a reducer aggregates by vote or score; no external effectors act until the answer is chosen.
- ·Hard reasoning, maths or code where a single pass is unreliable.
- ·High-stakes classification that needs a confidence signal.
- ·Any task where a majority of samples is more trustworthy than one.
- ·Markedly more reliable than a single pass on hard problems.
- ·Simple to implement: sample and vote.
- ·The agreement rate is a built-in confidence signal.
- ·No architectural coupling: it wraps any core.
- ·N times the cost and latency of a single pass.
- ·The majority can be confidently wrong on systematic errors.
- ·Only helps where sampling diversity is real.
- ·The aggregation rule (vote versus score) needs care.
Description, use cases and trade-offs: Field Book analysis, synthesised from established agent-architecture literature.
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