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[2602.15353] NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
Summary
The paper presents NeuroSymActive, a novel framework for Knowledge Graph Question Answering that integrates differentiable neural-symbolic reasoning with active exploration to improve accuracy and efficiency in answering knowledge-intensive queries.
Why It Matters
This research addresses the challenges faced by current language models in handling complex, knowledge-based queries. By combining neural and symbolic approaches, NeuroSymActive aims to enhance the performance of AI systems in tasks requiring structured reasoning, which is crucial for applications in AI and natural language processing.
Key Takeaways
- NeuroSymActive integrates neural-symbolic reasoning with active exploration.
- The framework improves accuracy in Knowledge Graph Question Answering tasks.
- It reduces the need for expensive graph lookups and model calls.
- Empirical results show strong performance on standard benchmarks.
- The approach prioritizes high-value path expansions for efficiency.
Computer Science > Computation and Language arXiv:2602.15353 (cs) [Submitted on 17 Feb 2026] Title:NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering Authors:Rong Fu, Yang Li, Zeyu Zhang, Jiekai Wu, Yaohua Liu, Shuaishuai Cao, Yangchen Zeng, Yuhang Zhang, Xiaojing Du, Chuang Zhao, Kangning Cui, Simon Fong View a PDF of the paper titled NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering, by Rong Fu and 11 other authors View PDF HTML (experimental) Abstract:Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowledge-intensive queries that require precise, structured multi-hop inference. Knowledge graphs provide a compact symbolic substrate for factual grounding, but integrating graph structure with neural models is nontrivial: naively embedding graph facts into prompts leads to inefficiency and fragility, while purely symbolic or search-heavy approaches can be costly in retrievals and lack gradient-based refinement. We introduce NeuroSymActive, a modular framework that combines a differentiable neural-symbolic reasoning layer with an active, value-guided exploration controller for Knowledge Graph Question Answering. The method couples soft-unification style symbolic modules with a neural path evaluator and a Monte-Carlo style exploration policy that priori...
