Verification-Aware Planning for Multi-Agent Systems

• URL: http://arxiv.org/abs/2510.17109v1
• Date: Mon, 20 Oct 2025 02:54:29 GMT
• Title: Verification-Aware Planning for Multi-Agent Systems
• Authors: Tianyang Xu, Dan Zhang, Kushan Mitra, Estevam Hruschka,
• Abstract summary: We present VeriMAP, a framework for multi-agent collaboration with verification-aware planning.<n>The planner decomposes tasks, models subtask dependencies, and encodes planner-defined passing criteria.<n>We show how verification-aware planning enables reliable coordination and iterative refinement in multi-agent systems.
• Score: 35.82875628010279
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) agents are increasingly deployed to tackle complex tasks, often necessitating collaboration among multiple specialized agents. However, multi-agent collaboration introduces new challenges in planning, coordination, and verification. Execution failures frequently arise not from flawed reasoning alone, but from subtle misalignments in task interpretation, output format, or inter-agent handoffs. To address these challenges, we present VeriMAP, a framework for multi-agent collaboration with verification-aware planning. The VeriMAP planner decomposes tasks, models subtask dependencies, and encodes planner-defined passing criteria as subtask verification functions (VFs) in Python and natural language. We evaluate VeriMAP on diverse datasets, demonstrating that it outperforms both single- and multi-agent baselines while enhancing system robustness and interpretability. Our analysis highlights how verification-aware planning enables reliable coordination and iterative refinement in multi-agent systems, without relying on external labels or annotations.

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