Fortytwo Protocol: Decentralized AI Inference

• Fortytwo Protocol is a decentralized AI framework that leverages swarm intelligence and peer-ranked consensus to deliver high-quality, resilient inferences.
• It employs dynamic pairwise tournament models and blockchain-based reputation to robustly filter responses and mitigate adversarial attacks.
• Empirical results demonstrate superior performance against majority voting methods, ensuring reliable and scalable decentralized AI collaboration.

The Fortytwo Protocol is a decentralized framework for AI inference that utilizes swarm intelligence, peer-ranked consensus, and reputation-weighted tournament aggregation to robustly filter and amplify the highest quality responses in distributed settings. It is specifically designed to address the limitations of monolithic, centrally-controlled AI systems—computational bottlenecks, vulnerability to adversarial attacks, and lack of openness—by reimagining distributed collaboration via a network of semi-autonomous nodes operating with heterogeneous models.

1. Swarm Inference Principles

Fortytwo's core design leverages distributed generation and judging capacities across multiple nodes, each of which may run distinct model architectures. Swarm inference replaces the typical majority voting aggregation with a dynamic system in which nodes offer candidate responses and engage in competitive pairwise ranking. Each node serves a dual role: generating responses and evaluating the quality of peer outputs.

This mechanism operationalizes collective intelligence such that the most accurate, complete, and resilient answers surface through tournament-like aggregation. Nodes are modular, with frameworks for cognitive tasks (LLMs, expert systems), auxiliary processing, pairwise ranking, and networking modules supporting encrypted peer-to-peer messaging, blockchain-based coordination, and pre/post-processing pipelines.

Entry into the swarm requires the successful completion of domain-specific calibration tests, enforcing compute-stake rather than financial stakes, thereby ensuring meaningful participation and initial Sybil resistance.

2. Peer-Ranked Consensus via Pairwise Tournament Models

Consensus is formed through distributed pairwise judgment rounds. For each inference task, nodes generate responses; these are then organized into random pairings for blind comparison by participating nodes. A typical comparison involves listing unique mistakes or deficiencies of each candidate response, following with a clear rationale for preference, and outputting a constrained reasoning chain (usually 50–100 tokens).

Pairwise results are aggregated using an extended Bradley-Terry tournament model. For any response $i$ and $j$, the probability that $i$ is judged better is given by:

$P(i \succ j) = \frac{\pi_i}{\pi_i + \pi_j}$

where $\pi_i$ reflects the latent quality score for response $i$. The log-likelihood of the complete pairing round is:

$$\ell(\boldsymbol{\pi}) = \sum_{i<j} \left[ w_{ij} \log \frac{\pi_i}{\pi_i + \pi_j} + (n_{ij} - w_{ij}) \log \frac{\pi_j}{\pi_i + \pi_j} \right]$$

where $w_{ij}$ is the number of times $i$ was preferred over $j$, and $n_{ij}$ is total comparisons between $i$ and $j$.

To enhance robustness, all votes are reputation-weighted; nodes with a history of accurate generation and consensus-aligned ranking exert greater influence:

$$\ell_{\text{weighted}}(\boldsymbol{\pi}) = \sum_{a \in \text{Nodes}} R_a \sum_{(i,j) \in \mathcal{C}_a} \left[ y_{ij}^{(a)} \log \frac{\pi_i}{\pi_i + \pi_j} + (1-y_{ij}^{(a)}) \log \frac{\pi_j}{\pi_i + \pi_j} \right]$$

where $R_a$ is node $a$'s reputation and $y_{ij}^{(a)}$ represents its ranking per pair.

3. On-Chain Reputation and Adaptive Influence

Node reputation is dynamically updated and stored on-chain, reflecting ongoing accuracy in both response generation and ranking alignment. Reputation evolves through an exponential moving average:

$$R_{t+1}^{(i)} = \alpha R_t^{(i)} + (1-\alpha) \cdot \text{Accuracy}_t^{(i)}$$

Ranking accuracy utilizes metrics such as Kendall's tau correlation between individual and consensus rankings. Reputation decays with inactivity and is slashed for persistent low performance or manipulative patterns. This creates a meritocratic environment—nodes must demonstrate quality to gain influence and sustain economic viability.

4. Sybil Resistance and Collusion Penalties

Fortytwo integrates robust Sybil resistance through proof-of-capability, requiring intensive calibration tests and ongoing computational accuracy to enable participation. This approach makes Sybil attacks uneconomical—multi-identity adversaries must repeatedly commit substantial compute and risk reputation slashing.

Collusion is monitored via statistical analysis of mutual support patterns:

$$c_{ij}(t) = \frac{\text{\# times } i \text{ ranked } j \text{ in top half}}{\text{\# rounds both participated}}$$

When mutual upranking exceeds null expectations, reputation penalties are applied exponentially:

$$w_{ij}^{\text{adjusted}} = w_j \cdot \exp\left(-\lambda \cdot \max(0, c_{ij} - \tau_{\text{collusion}})\right)$$

Nodes exhibiting abnormal activity—e.g., isolated upranking, deviation from historical group behavior—face downweighted influence and reward reduction. The Sybil profit equation indicates that adversarial gain is strictly less than cost at all reasonable attack scales:

$$\text{Revenue}_{\text{Sybil}}(k) \ll \text{Cost}_{\text{entry}}(k) + \text{Cost}_{\text{operation}}(k, t) + \text{Cost}_{\text{slashing}}(k, t)$$

5. Consensus Robustness and Fault Tolerance

Information-theoretic analysis demonstrates that $O(n \log n)$ random pairwise comparisons suffice to robustly extract rankings with high probability. The Bradley-Terry model's probabilistic aggregation tolerates intransitivity and noise. Unlike classical Byzantine Fault Tolerance (BFT), which is bounded at $n<3$ for adversarial node fractions, Fortytwo's adaptive reputation weightings permit sustained accuracy even at ~30% malicious node ratios, with gradual, not catastrophic, performance decay.

Adversarial robustness is empirically validated; prompt injection and extraneous noise cause only 0.12% accuracy degradation in Fortytwo versus up to 11% in single-model baselines. Reputation mechanisms naturally filter out manipulated responses, increasing resilience without dependence on centralized auditing.

6. Empirical Performance and Economic Analysis

Fortytwo achieves frontier results across challenging benchmarks, consistently outperforming majority voting and matching leading individual models. The following table summarizes its empirical metrics:

Benchmark	Fortytwo Accuracy	Majority Voting	Leading Individual Model
GPQA Diamond	85.90%	68.69%	87.70% (Grok 4)
LiveCodeBench	84.40%	65.40%	81.90%
MATH-500	99.60%	91.90%	99.40%
AIME 2024	100%	75.7%	94.3%
AIME 2025	96.66%	80.3%	94.3%
HLE	24.84%	11.90%	26.50%

Ablation studies confirm the necessity of all protocol features; omission of reasoning chains (-5.3%), temperature diversity (-10.1%), and multi-model ranking (-2.5%) each result in measurable accuracy loss. Swarm scaling benefits persist up to ~30 nodes, with accuracy plateauing thereafter and maintaining a strict advantage across all evaluated swarm sizes.

In terms of computational cost, Fortytwo (at 35 nodes) requires approximately 40× the resources of a single model inference—significantly less than ZKML (10,000×) and OPML (100×), suggesting a favorable cost–quality trade-off for real-world deployments.

7. Technical Significance and Future Implications

Fortytwo's salient features—swarm intelligence, meritocratic consensus, Sybil resistance, adversarial robustness, and decentralized operation—make it a candidate model for future trustless, scalable, and democratized AI services. Its architecture obviates the need for central authorities, supports dynamic adaptation of influence and rewards, and offers transparent, auditable consensus-building through blockchain record-keeping and explicit reasoning chains.

The protocol's ability to match and sometimes surpass monolithic frontier models, resist a wide spectrum of adversarial behaviors, and execute robust economic defenses against Sybil strategies positions it as a foundational blueprint for decentralized AI systems. A plausible implication is the potential for broad, trustless, and high-performance collective AI inference, mitigating the limitations associated with centralized infrastructure and opaque computation.

Summary Formulae:

• Pairwise preference:

$P(i \succ j) = \frac{\pi_i}{\pi_i + \pi_j}$

• Log-likelihood aggregation:

$$\ell(\boldsymbol{\pi}) = \sum_{i<j} \left[ w_{ij} \log \frac{\pi_i}{\pi_i + \pi_j} + (n_{ij} - w_{ij}) \log \frac{\pi_j}{\pi_i + \pi_j} \right]$$

• Reputation update:

$$R_{t+1}^{(i)} = \alpha R_t^{(i)} + (1-\alpha) \cdot \text{Accuracy}_t^{(i)}$$

Fortytwo demonstrates that distributed, collective mechanisms can reproduce or exceed individual state-of-the-art model performance while operationalizing essential security and democratizing access for the evolving AI ecosystem.