TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering (2026.findings-acl)
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| Challenge: | Existing methods for multihop Knowledge Graph Question Answering (KGQA) treat each reasoning step independently and fail to leverage experience from prior explorations, leading to fragmented reasoning and redundant exploration. |
| Approach: | They propose a framework that unifies LLM-driven contextual reasoning with exploration prior integration to enhance coherence and robustness of multihop KGQA. |
| Outcome: | Extensive experiments on multiple KGQA benchmarks show that TRACE outperforms state-of-the-art methods. |
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