Task-Conditioned Quality Learning for Heterogeneous and Unreliable Multi-Agent Systems

We study a heterogeneous multi-agent setting in which any open-world intelligent entity, including AI models, human-operated components, and hybrid systems, can be represented as a callable intelligence node. Such a setting expands the boundary of single-form intelligence, but it is difficult to orchestrate because the internal behavior and ability of nodes are usually not observable, making public descriptions an uncertain guide to actual execution quality. Deceptive profiles and collusive feedback can further distort task allocation. We propose HICS, a Heterogeneous Intelligence Coordination System that serves as an external routing and quality-control framework for such open node pools. HICS adopts a task encoder that learns the task geometry online from previously available feedback and maintains task-conditioned rolling quality expectations, complemented by a direct contextual estimate, to identify which nodes are likely to respond reliably under the current task condition. To facilitate learning, HICS supports separate answering and evaluation roles to obtain denser weak supervision, with reference feedback and audit anchors for calibration. We construct two complementary evaluation settings: a MedAgentBoard-based semi-synthetic medical benchmark for heterogeneous routing under partial outcome feedback, and controlled simulations that examine misleading profiles, hidden specialization, and collusive behavior. Comparisons and ablation studies assess routing quality, quality-model components, and the contribution of weak feedback. The results demonstrate the effectiveness and limits of task-conditioned reliability learning in the evaluated heterogeneous node pools.