SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs
arXiv:2512.04868v2 Announce Type: replace Abstract: Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing approaches often suffer from inaccuracies and prohibitive computational costs, particularly when processing intricate queries over large knowledge graphs. Specifically, large language models (LLMs) tend to generate syntactically invalid or semantically misaligned logical forms for complex multi-hop or aggregation queries, while conventional entit
The paper addresses current limitations of LLMs in complex reasoning over knowledge graphs, a critical bottleneck for advanced AI agent development, with a specific focus on conversational QA.
This development indicates progress towards more reliable and autonomous AI systems capable of understanding and reasoning over complex data, which is essential for numerous applications.
AI systems become more capable of accurate and context-aware responses to intricate queries over large knowledge bases, reducing errors and computational overhead.
- · AI developers
- · Enterprise search
- · Data analytics platforms
- · Knowledge graph technology providers
- · Manual data analysis
- · Inefficient current LLMs
- · Systems reliant on simple query matching
More sophisticated and human-like conversational AI assistants emerge with better reasoning capabilities.
Increased automation of complex information retrieval and decision-making processes across industries.
The development of truly autonomous AI agents capable of planning and executing tasks based on deep understanding of dynamic information.
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Read at arXiv cs.CL