Fortytwo: Swarm Inference with Peer-Ranked Consensus
Authors: name: Vladyslav Larin, name: Ihor Naumenko, name: Aleksei Ivashov, name: Ivan Nikitin, name: Alexander Firsov
Organization: Fortytwo

As centralized AI hits compute ceilings and diminishing returns from
ever-larger training runs, meeting demand requires an inference layer that
scales horizontally in both capacity and capability. We present Fortytwo, a
novel protocol that leverages swarm intelligence principles and distributed
pairwise ranking consensus to achieve superior performance in AI inference. Our
approach reimagines collaboration among AI nodes using swarm inference: a
peer-ranked, reputation-weighted consensus across heterogeneous models that
surfaces the highest-quality responses. Using pairwise ranking with a custom
Bradley-Terry-style aggregation model, we demonstrate that swarm inference
substantially outperforms majority voting, achieving 85.90% on GPQA Diamond
versus 68.69% for majority voting with the same model set - an improvement of
+17.21 percentage points (approximately +25.1% relative). The protocol
incorporates on-chain reputation so node influence adapts to demonstrated
accuracy over time, yielding a meritocratic consensus that filters low-quality
or malicious participants. To resist Sybil attacks, Fortytwo employs
proof-of-capability in its consensus: nodes must successfully complete
calibration/test requests and stake reputation to enter ranking rounds, making
multi-identity attacks economically unattractive while preserving openness.
Across six challenging benchmarks, including GPQA Diamond, LiveCodeBench, and
AIME, our evaluation indicates higher accuracy and strong resilience to
adversarial and noisy free-form prompting (e.g., prompt-injection degradation
of only 0.12% versus 6.20% for a monolithic single-model baseline), while
retaining practical deployability. Together, these results establish a
foundation for decentralized AI systems - democratizing access to high-quality
inference through collective intelligence without sacrificing reliability or
security.