When Should Agent Trust Be Conditional? Characterizing and Attacking Skill-Conditional Reputation in Agent Swarms
arXiv:2606.14200v1 Announce Type: new Abstract: 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 requiri
The proliferation of heterogeneous LLM agents and open platforms necessitates more nuanced trust mechanisms than simple global scores.
This research addresses a critical limitation in current AI agent management, enabling more efficient and specialized task routing that leverages agent strengths.
Agent reputation systems will evolve from monolithic scores to skill-conditional assessments, optimizing multi-agent system performance and specialization.
- · AI platform providers
- · Developers of specialized LLM agents
- · Businesses leveraging multi-agent systems
- · Providers of generalized, undifferentiated LLM agents
- · Systems relying on simplistic agent ranking
Improved efficiency and performance of distributed AI agent ecosystems.
Increased demand for specialized agents and platforms capable of managing complex trust relationships.
The emergence of new economic models for agent marketplaces based on granular skill validation and reputation.
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Read at arXiv cs.AI