Abstract
As open platforms increasingly route tasks among heterogeneous LLM agents—differing in base model, scaffold, and tool stack—whose competence varies sharply by skill: an agent excellent at one skill may be useless at another. The standard reputation approach summarizes each agent by a single global trust score, but that scalar is the wrong object here, because routing every task to the globally most-trusted agent leaves the value of specialization unclaimed.
We study skill-conditional trust $R(i | k)$—the trust to place in agent $i$ for a task requiring skill $k$, rather than one score per agent—and pose three falsifiable questions: when is conditioning worth it, how much cross-skill evidence should be borrowed, and whether that borrowing is safe. A controlled phase-diagram analysis answers the first two: conditional trust wins only in a specific regime—high agent heterogeneity, sparse per-skill evidence, and correlated skills—and the coupling strength $\eta$ that buys this data efficiency is dual-use, because the same cross-skill borrowing is also a laundering channel.
On a public benchmark of 14 genuinely heterogeneous AppWorld agents, real pools land inside the beneficial regime—a small but genuine gain, with the per-skill best agent genuinely changing across skills. We then show that an attacker with cheap evidence in one skill and none in a target skill hijacks the conditional router, driving routing regret from 0 to 0.94 on a pool our zero-cost Conditional Information Value Test (CIVT) rates GREEN—while the ungated trust verdict it contaminates reads -0.06 instead of the honest +0.19. A zero-evidence gate bounds the attack but does not eliminate it; we characterize the residual cost under an explicit budget. We do not claim Sybil-resistance—we quantify the trade-off.
Blogger's Review: This paper delves into the effective management of trust and reputation in multi-agent systems, particularly under significant skill heterogeneity. The proposed model of conditional trust provides a new perspective for optimizing task allocation. However, the potential threats from attackers remind us that ensuring system security remains a crucial challenge. Overall, the research offers valuable theoretical support and practical references for future LLM applications.