@inproceedings{liu-etal-2026-beyond-passive,
title = "Beyond Passive Debate: Intent-Driven Multi-Agent Deliberation for Reasoning under Incomplete Knowledge Graphs",
author = "Liu, Yongqiang and
Peng, Qiyao and
Liu, Binrong and
Chen, Jiawei and
Li, Xuewei and
Yuan, Ning",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.850/",
pages = "19695--19712",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Model (LLM) agents have established a promising paradigm for Knowledge Graph Question Answering (KGQA). However, their practical performance is severely constrained by the pervasive incompleteness of real-world knowledge graphs (KGs). To address this, Incomplete KGQA (IKGQA) has emerged as a vital task, yet existing multi-agent frameworks often suffer from a logic-misalignment effect, where passive collaboration leads to retrieval failure and hallucination propagation when encountering structural gaps. In this paper, we propose IDLC, an Intent-Driven Logical Compensation framework designed for faithful IKGQA. Unlike traditional reactive debate, IDLC formalizes reasoning as a proactive deliberation task within a Dec-POMDP. It introduces an Active KG Explorer that generates explicit Gap Descriptors to translate structural gaps into actionable retrieval goals, alongside a Logical Consistency Verifier that performs bi-directional anchoring to intercept step-wise semantic drift. The entire multi-agent policy is jointly optimized via reinforcement learning with a specialized logic-fidelity reward, aligning agent behaviors toward faithful reasoning paths. Extensive experiments on BRINK and IKG-CWQ/WebQSP benchmarks demonstrate that IDLC outperforms state-of-the-art baselines in both reasoning performance and step-wise logical fidelity. Furthermore, IDLC exhibits robust zero-shot transferability across various incompleteness levels while maintaining computational efficiency, confirming the effectiveness of intent-driven coordination in repairing fractured reasoning chains."
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<abstract>Large Language Model (LLM) agents have established a promising paradigm for Knowledge Graph Question Answering (KGQA). However, their practical performance is severely constrained by the pervasive incompleteness of real-world knowledge graphs (KGs). To address this, Incomplete KGQA (IKGQA) has emerged as a vital task, yet existing multi-agent frameworks often suffer from a logic-misalignment effect, where passive collaboration leads to retrieval failure and hallucination propagation when encountering structural gaps. In this paper, we propose IDLC, an Intent-Driven Logical Compensation framework designed for faithful IKGQA. Unlike traditional reactive debate, IDLC formalizes reasoning as a proactive deliberation task within a Dec-POMDP. It introduces an Active KG Explorer that generates explicit Gap Descriptors to translate structural gaps into actionable retrieval goals, alongside a Logical Consistency Verifier that performs bi-directional anchoring to intercept step-wise semantic drift. The entire multi-agent policy is jointly optimized via reinforcement learning with a specialized logic-fidelity reward, aligning agent behaviors toward faithful reasoning paths. Extensive experiments on BRINK and IKG-CWQ/WebQSP benchmarks demonstrate that IDLC outperforms state-of-the-art baselines in both reasoning performance and step-wise logical fidelity. Furthermore, IDLC exhibits robust zero-shot transferability across various incompleteness levels while maintaining computational efficiency, confirming the effectiveness of intent-driven coordination in repairing fractured reasoning chains.</abstract>
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%0 Conference Proceedings
%T Beyond Passive Debate: Intent-Driven Multi-Agent Deliberation for Reasoning under Incomplete Knowledge Graphs
%A Liu, Yongqiang
%A Peng, Qiyao
%A Liu, Binrong
%A Chen, Jiawei
%A Li, Xuewei
%A Yuan, Ning
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-beyond-passive
%X Large Language Model (LLM) agents have established a promising paradigm for Knowledge Graph Question Answering (KGQA). However, their practical performance is severely constrained by the pervasive incompleteness of real-world knowledge graphs (KGs). To address this, Incomplete KGQA (IKGQA) has emerged as a vital task, yet existing multi-agent frameworks often suffer from a logic-misalignment effect, where passive collaboration leads to retrieval failure and hallucination propagation when encountering structural gaps. In this paper, we propose IDLC, an Intent-Driven Logical Compensation framework designed for faithful IKGQA. Unlike traditional reactive debate, IDLC formalizes reasoning as a proactive deliberation task within a Dec-POMDP. It introduces an Active KG Explorer that generates explicit Gap Descriptors to translate structural gaps into actionable retrieval goals, alongside a Logical Consistency Verifier that performs bi-directional anchoring to intercept step-wise semantic drift. The entire multi-agent policy is jointly optimized via reinforcement learning with a specialized logic-fidelity reward, aligning agent behaviors toward faithful reasoning paths. Extensive experiments on BRINK and IKG-CWQ/WebQSP benchmarks demonstrate that IDLC outperforms state-of-the-art baselines in both reasoning performance and step-wise logical fidelity. Furthermore, IDLC exhibits robust zero-shot transferability across various incompleteness levels while maintaining computational efficiency, confirming the effectiveness of intent-driven coordination in repairing fractured reasoning chains.
%U https://aclanthology.org/2026.emnlp-main.850/
%P 19695-19712
Markdown (Informal)
[Beyond Passive Debate: Intent-Driven Multi-Agent Deliberation for Reasoning under Incomplete Knowledge Graphs](https://aclanthology.org/2026.emnlp-main.850/) (Liu et al., EMNLP 2026)
ACL