Fortytwo: Swarm Inference with Peer-Ranked Consensus
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.
69033a71274024e4098e3260
Vladyslav Larin
claimed_verified
2025-12-08T08:36:20.655Z
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Vladyslav Larin
larinv
user
larinv
69033a71274024e4098e3261
Ihor Naumenko
69033a71274024e4098e3262
Aleksei Ivashov
claimed_verified
2026-02-19T09:54:24.385Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/635789b84fba057662cd973a/SWbLjiySOZzBeZulI7amL.jpeg
Aleksei Ivashov
aivashov
user
aivashov
69033a71274024e4098e3263
Ivan Nikitin
claimed_verified
2025-12-05T08:30:33.734Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63aeda3a2314b93f9e706a68/s8cyqhR7c0G1pZefPjAGa.png
Ivan Nikitin
inikitin
user
inikitin
69033a71274024e4098e3264
Alexander Firsov
Fortytwo: Swarm Inference with Peer-Ranked Consensus
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.
Fortytwo, a novel protocol using swarm inference and distributed pairwise ranking consensus, outperforms majority voting and demonstrates higher accuracy and resilience in decentralized AI systems.
swarm intelligence distributed pairwise ranking consensus swarm inference peer-ranked reputation-weighted consensus heterogeneous models Bradley-Terry-style aggregation model GPQA Diamond majority voting on-chain reputation proof-of-capability Sybil attacks LiveCodeBench AIME prompt-injection degradation decentralized AI systems collective intelligence