Multi-agent systems have shown strong performance in domains with reliable verifiers such as coding, where a verifier can select among candidates generated in parallel. Long-horizon web search has no such verifier. Majority voting over independent runs cannot add an answer that none of the runs reaches. Agents that instead read each other's findings adopt an early candidate before alternatives are tested, a failure we call early consensus. We present ArcticSwarm, a multi-agent research architecture that separates evidence gathering from evidence integration. A gathering gate lets selected search tasks post findings to a shared board without reading it. An integration gate checks a candidate at three steps, and a failed check reopens search instead of only rejecting the candidate. On all 830 BrowseComp-Plus questions with the open-weight Qwen 3.5-27B, ArcticSwarm reaches 82.6%. MiroFlow reaches 70.6% under the same model, tools, and limits, and majority voting reaches 62.5% at the same token budget. Without the gathering gate, teams keep a wrong first candidate in 32.2% of runs instead of 22.3%, and accuracy drops by 3.8 points. Removing the integration gate lowers accuracy by 5.9 points. With GPT-5 on live-web BrowseComp, ArcticSwarm reaches 73.6%, above the published scores of GPT-5 (54.9%) and MiroFlow (63.4%). These results show that multi-agent search without a verifier benefits from deferring consensus until candidates are checked, instead of voting after search ends.

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