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Luobin Huang

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

DCTR: Dual-Constraint Subgraph Optimization for Knowledge Graph-based Retrieval-Augmented Generation

Yukun Cao
Zirui Xu
Dongyang Li
Zhihao Guo
Luobin Huang
LIsheng Wang

Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) shifts the contents of retrieval from narrative text to a relational knowledge network, empowering large language models (LLMs) to harness structured relationships between entities. However, conventional KG-RAG approaches are resource-intensive, requiring either query decomposition with multiple LLM rounds or parameterized static knowledge injection to update the model. Although subgraph reasoning aims to address these issues, most current methods are based on heuristic shortest path and multi-hop graph traversal algorithms. The retrieved subgraphs suffer from incompleteness and semantic drift, and neglect the interaction between subgraph and LLMs in terms of fine-grained structural semantics. We propose a dual-constraint subgraph optimization for KG-RAG (DCTR). It improves subgraph retrieval and generates high-quality subgraphs with structural integrity and information salience for LLMs. Specifically, it formulates subgraph generation as a two-stage graph-theoretic constrained optimization problem to create compact and complete pseudo-labels. Since these pseudo-labels are discrete, a smooth approximation is employed to convert them into a differentiable representation, thereby optimizing the retriever to highlight key information while extracting subgraphs. On two benchmark datasets, DCTR significantly enhances subgraph quality, achieving state-of-the-art performance in LLM reasoning.

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AAAI Conference 2026 Conference Paper

Mnemosyne: Accelerating Multi-Hop Question Answering via Cache Hit Order Fitting

Haizhou Du
Jiujiu Li
Dongyang Li
Luobin Huang
LIsheng Wang

Multi-Hop Question Answering (MHQA) requires step-by-step reasoning across multiple pieces of information to answer complex questions. The cache-aided Retrieval-Augmented Generation (RAG) can accelerate the process of external knowledge retrieval at each reasoning step for MHQA. However, existing methods focus on the internal structure and ignore the misalignment between the queries’ arrival order and cache hit order. To tackle this, we propose Mnemosyne, a cache hit order fitting method designed to accelerate the RAG progress for MHQA. Specifically, our cache-aware order fitting strategy adjusts the order of queries arrival via graph reordering to better align with the cache hit order, thereby reducing the likelihood of failed or unproductive retrieval attempts. The multi-granularity caching storage mechanism is designed to loosen the strict hit condition to multiple similar semantic matching modes, facilitating that relevant documents can still be retrieved. Experiments conducted on four multi-hop QA datasets demonstrate that Mnemosyne effectively reduces retrieval latency while enhancing task answer F1 score, achieving a superior trade-off between efficiency and effectiveness.

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AAAI Conference 2025 Conference Paper

DPCL-Diff：Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning

Yukun Cao
LIsheng Wang
Luobin Huang

Temporal knowledge graph (TKG) reasoning that infers future missing facts is an essential and challenging task. Predicting future events typically relies on closely related historical facts, yielding more accurate results for repetitive or periodic events. However, for future events with sparse historical interactions, the effectiveness of this method, which focuses on leveraging high-frequency historical information, diminishes. Recently, the capabilities of diffusion models in image generation have opened new opportunities for TKG reasoning. Therefore, we propose a graph node diffusion model with dual-domain periodic contrastive learning (DPCL-Diff). Graph node diffusion model (GNDiff) introduces noise into sparsely related events to simulate new events, generating high-quality data that better conforms to the actual distribution. This generative mechanism significantly enhances the model's ability to reason about new events. Additionally, the dual-domain periodic contrastive learning (DPCL) maps periodic and non-periodic event entities to Poincaré and Euclidean spaces, leveraging their characteristics to distinguish similar periodic events effectively. Experimental results on four public datasets demonstrate that DPCL-Diff significantly outperforms state-of-the-art TKG models in event prediction, demonstrating our approach's effectiveness. This study also investigates the combined effectiveness of GNDiff and DPCL in TKG tasks.

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ECAI Conference 2025 Conference Paper

Reinforced and Integrated Prompt Optimization Strategy for Emotion Recognition in Conversation

Yukun Cao
Luobin Huang
Zhihao Guo
Lisheng Wang

Emotion recognition in conversation (ERC) aims to identify the emotion expressed in each utterance within a multi-turn dialogue. In recent years, the widespread adoption of language models (LM) has spurred the development of various prompting paradigms as effective adaptation strategies, aligning LM training objectives with the specific requirements of ERC. However, while the hard prompt-based paradigm offers high human interpretability, it suffers from limited task adaptability due to its discrete nature. In contrast, the soft prompt-based paradigm sacrifices interpretability in favor of improved adaptability by optimizing continuous embedding vectors. Both paradigms generally adopt an invariant prompt across utterances, which restricts their ability to model contextual diversity and results in suboptimal adaptability of the instance. To address these issues, we propose a comprehensive prompting strategy that balances interpretability and adaptability, consisting of two components: reinforced prompt exploration for hard prompts and feature integration for soft prompts. In reinforced prompt exploration, a policy network is trained via reinforcement learning to explore the discrete prompt space under cold-start conditions, efficiently optimizing hard prompts and improving task-level adaptability while preserving interpretability. In feature integration for soft prompts, we incorporate rich semantic features to form contextually relevant soft prompts, assigning each utterance a distinct offset subspace to improve instance-level adaptability. Experiments on three datasets demonstrate that our prompting strategy achieves state-of-the-art performance in ERC.

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