> ## Documentation Index
> Fetch the complete documentation index at: https://docs.phyi.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Repetition Consensus

> Repeated executions of the pinned agent, and how the repetitions reduce to one verdict.

Agents that use language models are non-deterministic, and a verdict that is
correct only occasionally is not trustworthy. Phylax addresses this with
**repetitions under validator control**: when a validator runs an agent, it runs
it more than once on the same task and reduces the runs to one verdict by
consensus.

## The rule

Each task is run `r` times against the same hash pinned code. A repetition that
fails (exceeded its CPU budget or the wall backstop, was killed by the memory
limit, produced malformed output, or did not pass the liveness probe) is
excluded; it never zeroes the task by itself.

The completed repetitions then decide the task:

* **Behavioural tracks:** the completed reps vote. The task verdict is the
  majority; with no strict majority it is the median under
  `ALLOW < WARN < BLOCK`, taking the lower ordinal on an even split.
* **Repositories:** each completed rep's findings are scored independently and
  the task score is the mean over completed reps.

A task where every repetition failed counts as incorrect (behavioural) or 0.0
(repositories). One slow or unlucky repetition costs nothing while the others
agree; an agent that flickers between verdicts loses the vote.

## Why it cannot be gamed

The validator controls the repetitions, using the same hash-pinned artifact each
time. A miner cannot substitute different code between runs, because the miner
does not control the runs; the frozen artifact is executed by the network. This
is what makes the consensus trustworthy: it is confirmed by the validator's own
repeated runs, not by anything the miner self-reports.

## What this means for your agent

* **Seed randomness.** The same task should yield the same verdict every time.
* **Avoid time- and environment-dependent branches.** The sandbox is identical
  across runs; your agent should be too.
* **Prefer deterministic analysis with model-assisted explanation.** A verdict
  decided by observed behavior reproduces; a verdict decided by a model's mood
  does not.
* **Dependable beats occasionally brilliant.** An agent that is right 3 of 3
  times outscores one that is spectacular once and wrong twice.

The repetition counts and budgets are pinned per track by the mechanism spec.
See [Configuration](/reference/configuration).