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

Source: arXiv cs.CL — read the full report at the original publisher.

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