Why Fusion spends more tokens on purpose
Fusion uses more AI sessions than a single-agent build. Our own runs show what that extra verification caught, where it helped, and when one builder is the better buy.
topic · tournaments
Why the best AI-written code still needs an independent adversary
The first AI coding agents usually produced credible implementations. The biggest improvement came when fresh reviewers challenged the work, combined the best ideas, and tested the finished result through paths the builders missed.
Why we run judges from three different vendors
A jury drawn from rival model vendors breaks family bias: a model grading its sibling's homework is a conflict of interest. Cross-vendor votes, blind packets, and preserved dissent make the verdict neutral.
Why Fusion makes integrators compete too
Picking the best candidate leaves value on the table, but combining candidates creates new failure modes: dropped grafts, flattened dissent, new regressions. Fusion makes configured integrators produce competing complete finals, re-gates each one, and returns them to jury pass 2.
The win ledger: what we learn when the blinds come off
Judges stay blind during a run for fairness. After it closes, an outcome survey unblinds every role, and results accumulate into the win ledger: a per-engine, per-provider, per-model strength record that guides future roster choices.
The integrator: one final from many candidates
After judging, an integrator builds one complete final from the winning candidate plus approved grafts from the others, then re-runs the gate. You merge one thing, not a pile.
One task, rival agents, parallel lanes
Fusion runs the same task through competing coding agents in parallel lanes, then keeps only what survives judging. Competition is the quality mechanism, not a gimmick.