Papers with Adaptive Multi-Agent Trajectory Alignment framework
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering (2026.acl-long)
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Taolin Zhang, Dongyang Li, Chen Chen, Qizhou Chen, Jiuheng Wan, Xiaofeng He, Chengyu Wang, Richang Hong
| Challenge: | Empirical results show that AMATA outperforms baseline approaches, knowledge-augmented frameworks, and LLMs on knowledge-intensive QA benchmarks. |
| Approach: | They propose an Adaptive Multi-Agent Trajectory Alignment framework that integrates external knowledge to improve response interpretability and factual grounding. |
| Outcome: | The proposed framework outperforms baseline approaches, knowledge-augmented frameworks, and LLM-based trajectory systems on five established knowledge-intensive QA benchmarks. |