Fortytwo: Swarm Inference with Peer-Ranked Consensus (2510.24801v1)

Abstract: 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.

• The paper introduces a decentralized swarm-based inference protocol using dual-role nodes and peer-ranked consensus to overcome centralized AI limitations.
• The paper demonstrates improved benchmarking performance, with significant gains such as a 17.21 percentage point increase on GPQA Diamond compared to majority voting.
• The paper integrates compute-anchored Sybil resistance and reputation-weighted aggregation to maintain robustness even with up to 30% Byzantine nodes.

Fortytwo: Swarm Inference with Peer-Ranked Consensus

Introduction and Motivation

The Fortytwo protocol addresses the scalability, robustness, and democratization challenges inherent in centralized AI inference by introducing a decentralized, swarm-based inference architecture. The protocol leverages swarm intelligence, distributed pairwise ranking, and reputation-weighted consensus to orchestrate heterogeneous AI nodes for high-quality, trustless inference. The approach is motivated by the limitations of centralized AI—compute bottlenecks, lack of transparency, and vulnerability to single points of failure—and by the inadequacy of existing decentralized inference solutions, which often trade off between security, efficiency, and economic viability.

System Architecture

Dual-Role Node Design

Each node in Fortytwo is both a generator and a judge. Nodes generate candidate responses and participate in peer evaluation via pairwise ranking. This dual-role design aligns incentives: nodes are rewarded for both high-quality generation and accurate, consensus-aligned ranking. The architecture supports heterogeneous models, including large LLMs, domain-specific expert systems, and hybrid neural-symbolic agents. Nodes may also implement internal ensembles and auxiliary modules for pre/post-processing, tool integration, and caching.

Semantic Topology and Routing

Nodes are organized in a semantic embedding space, with axis-aligned hierarchical partitioning (akin to k-d trees) to cluster nodes by expertise. Query routing is performed by embedding the query and traversing the partition tree to select relevant sub-meshes, achieving $O(\log n)$ complexity. Bandwidth-aware coverage radii and dynamic sub-mesh formation ensure load balancing and efficient resource utilization.

Consensus Mechanism

Distributed Pairwise Ranking

Consensus is achieved via distributed pairwise ranking, where each node generates a set of pseudo-random pairwise comparisons (excluding self-comparisons) and provides multi-token reasoning chains for each decision. The protocol employs a custom Bradley–Terry aggregation model, which is robust to intransitive preferences and noisy or adversarial rankings. The use of multi-token reasoning (50–100 tokens per comparison) is empirically shown to improve ranking accuracy by 5.3% over single-token scoring, and provides valuable audit trails for system improvement.

Reputation-Weighted Aggregation

Node influence in consensus is dynamically weighted by on-chain reputation, which evolves based on historical accuracy in both generation and ranking. Reputation is updated via exponential moving averages of consensus wins and ranking alignment (e.g., Kendall’s tau with final consensus). Poor performance triggers reputation slashing, analogous to stake slashing in PoS systems, while inactivity leads to decay. This meritocratic weighting filters out low-quality or malicious nodes and provides resilience beyond classical BFT bounds.

Adversarial and Byzantine Robustness

The protocol demonstrates strong resilience to adversarial and Byzantine nodes. Collusion is detected via voting pattern analysis, with exponential penalties for mutual support above expected baselines. The system tolerates up to 30% Byzantine nodes with graceful degradation, exceeding the classical $n \geq 3f+1$ BFT limit due to dynamic reputation weighting. Under prompt injection and extraneous information attacks, Fortytwo exhibits only 0.12% accuracy degradation, compared to 6.2% for monolithic models.

Sybil Resistance: Compute Stake Mechanism

Fortytwo introduces a compute-anchored Sybil defense, requiring nodes to demonstrate capability via comprehensive test requests across claimed domains. Entry is gated by passing dynamically generated, domain-diverse test suites, with computational cost serving as a natural barrier to identity multiplication. This approach avoids the capital concentration and resource waste of PoW/PoS, and is more robust than social graph or personhood-based defenses. Reputation is non-transferable and cryptographically bound to node identity, preventing reputation markets and ensuring long-term alignment.

Empirical Evaluation

Benchmark Results

Fortytwo is evaluated on six challenging benchmarks, including GPQA Diamond, LiveCodeBench, MATH-500, AIME 2024/2025, and HLE. The protocol achieves:

• GPQA Diamond: 85.90% (vs. 68.69% for majority voting; +17.21pp, +25.1% relative)
• LiveCodeBench: 84.40%
• MATH-500: 99.60%
• AIME 2024: 100%
• AIME 2025: 96.66%
• HLE: 24.84%

Performance is competitive with or superior to state-of-the-art monolithic models (e.g., xAI Grok 4, GPT-5 Thinking), with more consistent results across domains.

Ablation and Scaling

Ablation studies confirm the criticality of multi-token reasoning, temperature diversity, and reputation-weighted ranking. Swarm size scaling shows rapid accuracy gains up to ~30 nodes, with diminishing returns thereafter. The protocol consistently outperforms majority voting at all scales.

Robustness and Economic Analysis

Fortytwo maintains high accuracy under adversarial prompting and with up to 30% Byzantine nodes. The computational overhead is 40× that of a single model inference (for 35 nodes), but is orders of magnitude more efficient than ZKML or OPML approaches. The compute stake mechanism renders Sybil attacks economically unviable for $k > 1$ identities under realistic parameterizations.

Theoretical and Practical Implications

Cognitive and Statistical Foundations

The protocol’s success is rooted in the cognitive superiority of pairwise comparison (Thurstone’s Law), the statistical efficiency of Bradley–Terry aggregation, and the emergent robustness of diverse ensembles. Multi-token reasoning enforces deliberative, System 2-style evaluation, reducing hallucinations and bias. The system’s evolutionary dynamics—where reputation accrues to high performers and is slashed for poor or malicious behavior—drive continual improvement and antifragility.

Limitations

Latency is increased due to multiphase consensus, which may be unsuitable for real-time applications. Interpretability remains a challenge, as global rankings are relative and debugging requires sophisticated analysis. The protocol’s efficiency and robustness depend on sufficient node diversity and honest participation.

Future Directions

Potential extensions include alternative preference aggregation models (e.g., Plackett-Luce), cross-modal consensus (vision, audio), cryptographic commitments for reduced latency, and human-in-the-loop calibration. Theoretical analysis of optimal swarm size, diversity, and robustness guarantees remains an open area.

Conclusion

Fortytwo demonstrates that decentralized, swarm-based inference with peer-ranked consensus can achieve and surpass the performance of monolithic models, while providing superior robustness, democratization, and security. The protocol’s combination of distributed pairwise ranking, multi-token reasoning, compute stake Sybil resistance, and dynamic reputation weighting establishes a practical foundation for trustless, high-quality AI inference. The results suggest that the future of scalable, reliable AI may lie in orchestrated collectives of diverse, specialized nodes rather than ever-larger centralized models. This paradigm has significant implications for the accessibility, transparency, and resilience of AI systems, and provides a compelling template for future research and deployment in decentralized AI.