Learning to Recommend Multi-Agent Subgraphs from Calling Trees

• URL: http://arxiv.org/abs/2601.22209v1
• Date: Thu, 29 Jan 2026 18:26:12 GMT
• Title: Learning to Recommend Multi-Agent Subgraphs from Calling Trees
• Abstract summary: Multi-agent systems (MAS) increasingly solve complex tasks by orchestrating agents and tools selected from rapidly growing marketplaces.<n>We introduce a generic textbfconstrained recommendation framework that first uses retrieval to build a compact candidate set conditioned on the current subtask and context.<n>We ground both the formulation and learning signals in textbfhistorical calling trees, which capture the execution structure of MAS.
• Score: 6.247621896325622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent systems (MAS) increasingly solve complex tasks by orchestrating agents and tools selected from rapidly growing marketplaces. As these marketplaces expand, many candidates become functionally overlapping, making selection not just a retrieval problem: beyond filtering relevant agents, an orchestrator must choose options that are reliable, compatible with the current execution context, and able to cooperate with other selected agents. Existing recommender systems -- largely built for item-level ranking from flat user-item logs -- do not directly address the structured, sequential, and interaction-dependent nature of agent orchestration. We address this gap by \textbf{formulating agent recommendation in MAS as a constrained decision problem} and introducing a generic \textbf{constrained recommendation framework} that first uses retrieval to build a compact candidate set conditioned on the current subtask and context, and then performs \textbf{utility optimization} within this feasible set using a learned scorer that accounts for relevance, reliability, and interaction effects. We ground both the formulation and learning signals in \textbf{historical calling trees}, which capture the execution structure of MAS (parent-child calls, branching dependencies, and local cooperation patterns) beyond what flat logs provide. The framework supports two complementary settings: \textbf{agent-level recommendation} (select the next agent/tool) and \textbf{system-level recommendation} (select a small, connected agent team/subgraph for coordinated execution). To enable systematic evaluation, we construct a unified calling-tree benchmark by normalizing invocation logs from eight heterogeneous multi-agent corpora into a shared structured representation.

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