Papers by Ruifan Wu
Semantic XPath: Structured Agentic Memory Access for Conversational AI (2026.acl-demo)
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| Challenge: | Early ConvAI agents rely on an in-context approach that appends the growing conversation history to the model input, but this approach scales poorly under context-window limits. |
| Approach: | They propose a tree-structured memory module to access and update structured conversational memory. |
| Outcome: | The proposed system improves over flat-RAG baselines while using only 9.1% of the tokens required by in-context memory. |
Progressive Planning and Reinforced Reasoning: Large Language Model-Guided Multi-hop Question Answering over Knowledge Graph (2026.findings-acl)
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| Challenge: | Existing approaches to multi-hop question answering lack effective intermediate guidance and policy networks focus on local neighborhood information, making it difficult to anticipate the long-term consequences of decisions. |
| Approach: | They propose a framework that converts decomposed sub-question sequences into stepwise decision guidance and a structure-aware lookahead policy network to enhance the agent's global state awareness and decision foresight in complex environments. |
| Outcome: | The proposed framework surpasses state-of-the-art methods while showing strong generalization. |