Re: CLEF-2026 CheckThat! Lab -- 2nd Call for Participation

Engineering Trust Networks for Under trustless adversarial Pressure with
decentralized virtual cooperation.
Abstract

Collective intelligence platforms fail when trust formation is treated as
community management rather than adversarial engineering. In practice,
corruption pressure arrives through many channels simultaneously: identity
forgery (Sybils), social capture, coercion/kompromat, incentive
manipulation, and bridge-node compromise. We propose a trust-network
architecture for information access systems that combines multi-dimensional
reputation, topology constraints that enforce minimum trusted connectivity
before authority accrues, and explicit bridge-node hardening. We contribute
an evaluation framework that measures capture-resilience: the system’s
ability to preserve truthful, high-utility information access as
adversaries adapt. Using an agent-based simulation protocol, we test how
reputation dimensionality and connectivity constraints interact with
infiltration rates to suppress capture while maintaining inclusion. The
result is a concrete design pattern for trust networks that support
collective intelligence without collapsing into covert centralization or
plutocracy.
1. Introduction

Collective intelligence must emerge both top-down and bottom-up. Top-down
structures provide standards, interfaces, and auditability. Bottom-up
participation provides peer legitimacy, local adaptation, and error
correction. Real systems, however, face continuous corruption pressure from
multiple vectors, not a single failure mode.

Modern information access systems — search, retrieval, and conversational
AI — now function as coordination infrastructures. If these infrastructures
are captured, the collective intelligence they support degrades. A central
vulnerability across socio-technical systems is the bridge node:
individuals or entities that connect communities, disciplines, or
governance layers. While essential for knowledge flow, bridge nodes are
also the most efficient targets for manipulation and capture.

This paper proposes an engineered trust network specifically designed for
adversarial environments where infiltration, Sybil attacks, and
reputational gaming are expected rather than exceptional.
2. Threat Model

We assume persistent adversarial pressure including Sybil identity
creation, targeted social capture, bridge-node compromise, reputation
gaming, and topology manipulation. The system must remain functional under
adaptive adversaries rather than static attack assumptions.

Unlike idealized decentralized models, we explicitly assume:
(1) Corruption can originate internally or externally,
(2) Reputation systems will be gamed,
(3) High-centrality actors will be targeted first,
(4) Trust must be continuously validated rather than statically assigned.
3. Architecture: Multi-Dimensional Trust Networks

We propose replacing scalar reputation with a multi-dimensional trust
vector R composed of integrity, independence, social integration, domain
competence, and behavioral stability. No single metric is sufficient for
Sybil resistance or governance legitimacy.

Hard topology constraints are introduced:
- Minimum trusted connectivity before authority accrues
- Diversity of attestations across independent clusters
- Time-matured trust edges rather than instant elevation

Bridge nodes are hardened through rotation, compartmentalization of
authority, multi-party approvals, and continuous audit telemetry. This
converts bridge-node capture from a low-cost attack into a high-cost,
detectable process.
4. Evaluation Framework

We introduce capture-resilient evaluation aligned with experimental
information access research. The system is evaluated not only on retrieval
quality and conversational utility, but also on resilience metrics under
simulated adversarial pressure.

Key metrics include:
- Retrieval accuracy under infiltration
- Bridge-node capture rate
- Sybil elevation success probability
- Time-to-compromise
- Utility degradation curves
- Recovery latency after revocation and rotation

Simulations employ agent-based models with community structure, adaptive
adversaries, and dynamic trust recalibration.
5. Reproducibility and Experimental Design

All simulations use deterministic seeds, parameter manifests, and
reproducible evaluation pipelines. The framework supports Latin Hypercube
parameter sweeps across infiltration rate, topology density, reputation
dimensionality, and detection sensitivity.

Open-science principles are followed through anonymized repositories,
reproducible scripts, and complete configuration disclosure compatible with
double-blind academic review standards.
6. Discussion

Token-weighted or wealth-weighted governance systems naturally drift toward
plutocracy. Multi-dimensional trust weighting mitigates this by
incorporating non-wealth integrity signals and peer validation.

However, overly strict admission thresholds can reduce inclusion and
innovation. Therefore, dynamic threshold calibration is required, balancing
security and openness. The objective is not ideological decentralization,
but measurable resilience and sustained collective intelligence.

Collective intelligence is best understood as an emergent property of
peer-to-peer language, shared epistemic standards, and structured trust
constraints augmented by intelligent systems.
7. Conclusion

Parallel trust networks are not sustained by rhetoric but by engineered
constraints that make corruption expensive, visible, and recoverable. By
integrating multi-dimensional reputation, topology-aware governance, and
bridge-node protection, information access systems can maintain integrity
under adversarial pressure. This framework aligns with the goals of robust,
evaluation-driven information retrieval and conversational intelligence
systems, offering a scalable foundation for resilient collective
intelligence.