AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering

• URL: http://arxiv.org/abs/2605.17352v1
• Date: Sun, 17 May 2026 09:45:24 GMT
• Title: AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering
• Abstract summary: AMATA is an Adaptive Multi-Agent Trajectory Alignment framework.<n>It dynamically integrates external knowledge to improve response interpretability and factual grounding.<n>We introduce two principal innovations: (1) Intra-Trajectory Preference Learning, which learns objective-oriented preferences to prioritize critical agents, and (2) Inter-Agent Dependency Learning, which captures cross-agent tool dependencies.
• Score: 59.145044878490815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remains challenging. These difficulties are primarily due to hallucinations and the limitations of LLMs in bridging long-tail knowledge gaps. To address this, we propose AMATA, an Adaptive Multi-Agent Trajectory Alignment framework that dynamically integrates external knowledge to improve response interpretability and factual grounding. Our architecture leverages six specialized agents that collaboratively perform structured actions for complex question reasoning. We formalize multi-agent collaboration with external tools as a trajectory preference alignment problem, incorporating question-aware agent customization and inter-agent preference harmonization. AMATA introduces two principal innovations: (1) Intra-Trajectory Preference Learning, which learns objective-oriented preferences to prioritize critical agents, and (2) Inter-Agent Dependency Learning, which captures cross-agent tool dependencies through a novel dependency-aware direct preference optimization technique. Empirical results show that AMATA consistently outperforms baseline approaches, knowledge-augmented frameworks, and LLM-based trajectory systems on five established knowledge-intensive QA benchmarks. Further analysis demonstrates the efficiency of our method in reducing token consumption.

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