The Inverse-Wisdom Law: Architectural Tribalism and the Consensus Paradox in Agentic Swarms

2026-04-29

AI Summary

This work addresses the “consensus paradox” in multi-agent systems, where homogeneous architectures lead collectives to converge on internally consistent yet factually incorrect conclusions, thereby reinforcing erroneous reasoning trajectories. The study introduces the “Law of Inverse Wisdom,” demonstrating that increasing the number of logical agents can paradoxically stabilize false outcomes. Novel metrics—including the tribal coefficient and sycophancy weight—are proposed to quantify these dynamics. Empirical evaluation across GAIA, Multi-Challenge, and SWE-bench benchmarks, comprising 12,804 reasoning trajectories generated by Gemini 3.1 Pro, Claude Sonnet 4.6, and GPT-5.4, reveals that final system consensus is governed primarily by the synthesizer’s reception logic, with architectural homogeneity identified as the principal cause of failure. These findings establish the “Principle of Enforced Heterogeneity” as a foundational safeguard for building reliable multi-agent systems.

Abstract

As AI transitions toward multi-agent systems (MAS) to solve complex workflows, research paradigms operate on the axiomatic assumption that agent collaboration mirrors the "Wisdom of the Crowd". We challenge this assumption by formalizing the Consensus Paradox: a phenomenon where agentic swarms prioritize internal architectural agreement over external logical truth. Through a 36 experiments encompassing 12,804 trajectories across three state-of-the-art (SOTA) benchmarks (GAIA, Multi-Challenge, and SWE-bench), we prove the Inverse-Wisdom Law: in kinship-dominant swarms, adding logical agents increases the stability of erroneous trajectories rather than the probability of truth. The introduction of additional logical audits converges the system toward a Logic Saturation where internal entropy hits zero while factual error hits unity. By evaluating the interaction between the 3 preeminent SOTA models (Gemini 3.1 Pro, Claude Sonnet 4.6, and GPT-5.4), we establish the Architectural Tribalism Asymmetry as a mechanistic law of transformer weights. We demonstrate that terminal swarm integrity is strictly gated by the synthesizer's receptive logic, rather than aggregate agent quality. We define the Tribalism Coefficient and the Sycophantic Weight as the primary mechanistic determinants of swarm failure. Finally, we establish the Heterogeneity Mandate as a foundational safety requirement for resilient agentic architectures.

Problem

• Consensus Paradox
• Architectural Tribalism
• Agentic Swarms
• Inverse-Wisdom Law
• Logic Saturation

Innovation

• Inverse-Wisdom Law
• Consensus Paradox
• Architectural Tribalism
• Logic Saturation
• Heterogeneity Mandate