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Haoran Luo

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10 papers

1 author row

JBHI Journal 2026 Journal Article

CAMM: Confidence-Aligned Multiview Multimodal Fusion for Brain Disorders Prediction With Imaging Transcriptomics

Haoran Luo
Zhoujie Fan
Wei Li
Hong Liang
Chen Jason Zhang
Xiaoyong Wei
Zheng Wang
Shan Cong

Brain disorder prediction can be enhanced by models that capture not only imaging phenotypes but also their underlying molecular context. Neuroimaging provides detailed structural and functional information, yet it offers limited insight into the gene-regulated processes driving these alterations. Transcriptomic atlases offer such molecular insights but are rarely available at the subject level due to invasive sampling. To address this gap, we propose CAMM, a confidence-aware multi-modal framework that integrates transcriptomic priors with imaging features to embed molecular context before fusion. CAMM further introduces a unified confidence calibration–regularization strategy that adapts modality contributions at the sample level, ensuring that information from high-confidence samples is leveraged to improve predictions for low-confidence samples, thereby enhancing robustness. Applied to large neuroimaging cohorts, CAMM consistently surpasses state-of-the-art baselines and identifies biologically meaningful biomarkers, demonstrating how transcriptomic priors can bridge molecular mechanisms and imaging for interpretable precision modeling of brain disorders.

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AAAI Conference 2026 Conference Paper

From Stimuli to Minds: Enhancing Psychological Reasoning in LLMs via Bilateral Reinforcement Learning

Yichao Feng
Haoran Luo
Lang Feng
Shuai Zhao
Anh Tuan Luu

Large Language Models show promise in emotion understanding, social reasoning, and empathy, yet struggle with psychologically grounded tasks requiring inference of implicit mental states in complex, socially and contextually ambiguous settings. These limitations stem from lacking theory-aligned supervision and difficulty capturing nuanced mental processes in real-world narratives. To bridge this gap, we leverage expert-labeled scenarios and propose a trajectory-aware reinforcement learning framework imitating expert psychological reasoning. By integrating real-world stimuli with structured reasoning guidance, our approach enables compact models to internalize social-cognitive principles, perform nuanced inference, and support continual self-improvement. Experiments across benchmarks show expert-level interpretive capability across psychological tasks.

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NeurIPS Conference 2025 Conference Paper

Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning

Haolin Pan
Hongyu Lin
Haoran Luo
Yang Liu
Kaichun Yao
Libo Zhang
Mingjie Xing
Yanjun Wu

Compiler auto-tuning optimizes pass sequences to improve performance metrics such as Intermediate Representation (IR) instruction count. Although recent advances leveraging Large Language Models (LLMs) have shown promise in automating compiler tuning, two significant challenges still remain: the absence of high-quality reasoning datasets for agents training, and limited effective interactions with the compilation environment. In this work, we introduce Compiler-R1, the first reinforcement learning (RL)-driven framework specifically augmenting LLM capabilities for compiler auto-tuning. Compiler-R1 features a curated, high-quality reasoning dataset and a novel two-stage end-to-end RL training pipeline, enabling efficient environment exploration and learning through an outcome-based reward. Extensive experiments across seven datasets demonstrate Compiler-R1 achieving an average 8. 46\% IR instruction count reduction compared to opt -Oz, showcasing the strong potential of RL-trained LLMs for compiler optimization. Our code and datasets are publicly available at https: //github. com/Panhaolin2001/Compiler-R1.

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NeurIPS Conference 2025 Conference Paper

HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation

Haoran Luo
Haihong E
Guanting Chen
Yandan Zheng
Xiaobao Wu
Yikai Guo
Qika Lin
Yu Feng

Standard Retrieval-Augmented Generation (RAG) relies on chunk-based retrieval, whereas GraphRAG advances this approach by graph-based knowledge representation. However, existing graph-based RAG approaches are constrained by binary relations, as each edge in an ordinary graph connects only two entities, limiting their ability to represent the n-ary relations (n >= 2) in real-world knowledge. In this work, we propose HyperGraphRAG, the first hypergraph-based RAG method that represents n-ary relational facts via hyperedges. HyperGraphRAG consists of a comprehensive pipeline, including knowledge hypergraph construction, retrieval, and generation. Experiments across medicine, agriculture, computer science, and law demonstrate that HyperGraphRAG outperforms both standard RAG and previous graph-based RAG methods in answer accuracy, retrieval efficiency, and generation quality.

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AAAI Conference 2025 Conference Paper

TSVC: Tripartite Learning with Semantic Variation Consistency for Robust Image-Text Retrieval

Shuai Lyu
Zijing Tian
Zhonghong Ou
Yifan Zhu
Xiao Zhang
Qiankun Ha
Haoran Luo
Meina Song

Cross-modal retrieval maps data under different modalities via semantic relevance. Existing approaches implicitly assume that data pairs are well-aligned and ignore the widely existing annotation noise, i.e., noisy correspondence (NC). Consequently, it inevitably causes performance degradation. Despite attempts that employ the co-teaching paradigm with identical architectures to provide distinct data perspectives, the differences between these architectures primarily stem from random initialization. Thus, the model becomes increasingly homogeneous along with the training process. Consequently, the additional information brought by this paradigm is severely limited. In order to resolve this problem, we introduce Tripartite Learning with Semantic Variation Consistency (TSVC) for robust image-text retrieval. We design a tripartite cooperative learning mechanism comprising a Coordinator, a Master, and an Assistant model. The Coordinator distributes data, and the Assistant model supports the Master model's noisy label prediction with diverse data. Moreover, we introduce a soft label estimation method based on mutual information variation, which quantifies the noise in new samples and assigns corresponding soft labels. We also present a new loss function to enhance robustness and optimize training effectiveness. Extensive experiments on three widely used datasets demonstrate that, even at increasing noise ratios, TSVC exhibits significant advantages in retrieval accuracy and maintains stable training performance.

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JBHI Journal 2024 Journal Article

GREMI: An Explainable Multi-Omics Integration Framework for Enhanced Disease Prediction and Module Identification

Hong Liang
Haoran Luo
Zhiling Sang
Miao Jia
Xiaohan Jiang
Zheng Wang
Shan Cong
Xiaohui Yao

Multi-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort.

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NeurIPS Conference 2024 Conference Paper

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction

Haoran Luo
Haihong E
Yuhao Yang
Tianyu Yao
Yikai Guo
Zichen Tang
Wentai Zhang
Shiyao Peng

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging and output merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available.

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AAAI Conference 2023 Conference Paper

DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing

Haoran Luo
Haihong E
Ling Tan
Gengxian Zhou
Tianyu Yao
Kaiyang Wan

In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a single view are limited in application because they weaken the hierarchical structure that represents the affiliation between entities. To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities. This paper defines link prediction and entity typing tasks on DH-KG for the first time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms baseline models on DH-KG, according to experimental results. Finally, we provide an example of how this technology can be used to treat hypertension. Our model and new datasets are publicly available.

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AAAI Conference 2023 Conference Paper

NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs

Haoran Luo
Haihong E
Yuhao Yang
Gengxian Zhou
Yikai Guo
Tianyu Yao
Zichen Tang
Xueyuan Lin

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n≥2) containing more than two entities, which are more prevalent in the real world. Moreover, previous CQA methods can only make predictions for a few given types of queries and cannot be flexibly extended to more complex logical queries, which significantly limits their applications. To overcome these challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs), which include massive n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries, including existential quantifiers (∃), conjunction (∧), disjunction (∨), and negation (¬). We also propose a parallel processing algorithm that can train or predict arbitrary n-ary FOL queries in a single batch, regardless of the kind of each query, with good flexibility and extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and other standard CQA datasets show that NQE is the state-of-the-art CQA method over HKGs with good generalization capability. Our code and dataset are publicly available.

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NeurIPS Conference 2023 Conference Paper

TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph

Xueyuan Lin
Haihong E
Chengjin Xu
Gengxian Zhou
Haoran Luo
Tianyi Hu
Fenglong Su
Ningyuan Li

Multi-hop logical reasoning over knowledge graph plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding methods for reasoning focus on static KGs, while temporal knowledge graphs have not been fully explored. Reasoning over TKGs has two challenges: 1. The query should answer entities or timestamps; 2. The operators should consider both set logic on entity set and temporal logic on timestamp set. To bridge this gap, we introduce the multi-hop logical reasoning problem on TKGs and then propose the first temporal complex query embedding named Temporal Feature-Logic Embedding framework (TFLEX) to answer the temporal complex queries. Specifically, we utilize fuzzy logic to compute the logic part of the Temporal Feature-Logic embedding, thus naturally modeling all first-order logic operations on the entity set. In addition, we further extend fuzzy logic on timestamp set to cope with three extra temporal operators ( After, Before and Between ). Experiments on numerous query patterns demonstrate the effectiveness of our method.

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