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Zhengke Gui

2 accepted papers

2026

Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction

AAAI 2026technical

Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs. Previous works on training LLMs to leverage external retrievers for solving complex problems have predominantly employed end-to-end reinforcement learning. However, these approache

Cited by 0SourcePDFScholar
2025

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching

EMNLP 2025

Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA). We attribute this to the semantic gap between structured knowledge