Selected work

Representative Papers

Geometric construction of k-optimal locally repairable codes

May 29, 2026

This work addresses the efficiency and optimality of locally repairable codes (LRCs) in distributed storage by systematically investigating LRCs with disjoint repair sets through a parity-check matrix approach. Leveraging projective geometry PG(2,q), partial r-spread structures, and a newly introduced s-Pasch configuration, the study provides the first geometric characterization for the existence of LRCs with locality three and minimum distance five. Furthermore, it constructs a family of q-ary k-optimal LRCs achieving minimum distance six for arbitrary locality r, attaining the theoretical bound on code length versus minimum distance and thereby realizing parameter optimality.

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ALEX:A Light Editing-knowledge Extractor

Nov 17, 2025

To address the static knowledge limitation of large language models (LLMs) and their difficulty in adapting to dynamic information, this paper proposes ALEX, a lightweight knowledge editing framework. Its core innovation is a novel hierarchical memory architecture that organizes knowledge via semantic clustering and integrates an inference-aware query synthesis (IQS) module with a dynamic evidence adjudication (DEA) engine, enabling efficient two-stage retrieval and multi-step reasoning alignment. This design reduces knowledge retrieval complexity from O(N) to O(K + N/C), significantly enhancing scalability and efficiency. On the MQUAKE benchmark, ALEX achieves substantial improvements in both multi-hop answer accuracy (MultiHop-ACC) and hop-wise reasoning path reliability (HopWise-ACC), while compressing the search space by over 80%. The framework thus provides a scalable, high-precision, and lightweight solution for dynamic knowledge updating.

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HI-Series Algorithms A Hybrid of Substance Diffusion Algorithm and Collaborative Filtering

Mar 03, 2025

Recommender systems face a long-standing trade-off between accuracy and diversity: Item-based Collaborative Filtering (ItemCF) enhances diversity at the expense of accuracy, while network-based diffusion methods (e.g., Mass Diffusion, MD) prioritize accuracy but degrade diversity. To address this, we propose the HI family of nonlinear hybrid algorithms—a scalable, unified framework that adaptively integrates ItemCF with multiple diffusion models (MD, HHP, BHC, BD) via a learnable parameter ε. Unlike prior linear ensembles, HI employs nonlinear weighting to preserve complementary strengths across paradigms, enabling simultaneous optimization of recommendation quality and novelty under both sparse and dense data regimes. Evaluated on benchmark datasets (e.g., MovieLens), HI achieves consistent improvements: F1-score gains of 0.8–5.2%, up to 18.6% higher Diversity@20, and markedly enhanced robustness and novelty in sparse settings.

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Recent publications

Latest Papers

Geometric construction of k-optimal locally repairable codes

May 29, 2026

This work addresses the efficiency and optimality of locally repairable codes (LRCs) in distributed storage by systematically investigating LRCs with disjoint repair sets through a parity-check matrix approach. Leveraging projective geometry PG(2,q), partial r-spread structures, and a newly introduced s-Pasch configuration, the study provides the first geometric characterization for the existence of LRCs with locality three and minimum distance five. Furthermore, it constructs a family of q-ary k-optimal LRCs achieving minimum distance six for arbitrary locality r, attaining the theoretical bound on code length versus minimum distance and thereby realizing parameter optimality.

0 citationsRead paper

ALEX:A Light Editing-knowledge Extractor

Nov 17, 2025

To address the static knowledge limitation of large language models (LLMs) and their difficulty in adapting to dynamic information, this paper proposes ALEX, a lightweight knowledge editing framework. Its core innovation is a novel hierarchical memory architecture that organizes knowledge via semantic clustering and integrates an inference-aware query synthesis (IQS) module with a dynamic evidence adjudication (DEA) engine, enabling efficient two-stage retrieval and multi-step reasoning alignment. This design reduces knowledge retrieval complexity from O(N) to O(K + N/C), significantly enhancing scalability and efficiency. On the MQUAKE benchmark, ALEX achieves substantial improvements in both multi-hop answer accuracy (MultiHop-ACC) and hop-wise reasoning path reliability (HopWise-ACC), while compressing the search space by over 80%. The framework thus provides a scalable, high-precision, and lightweight solution for dynamic knowledge updating.

0 citationsRead paper

HI-Series Algorithms A Hybrid of Substance Diffusion Algorithm and Collaborative Filtering

Mar 03, 2025

Recommender systems face a long-standing trade-off between accuracy and diversity: Item-based Collaborative Filtering (ItemCF) enhances diversity at the expense of accuracy, while network-based diffusion methods (e.g., Mass Diffusion, MD) prioritize accuracy but degrade diversity. To address this, we propose the HI family of nonlinear hybrid algorithms—a scalable, unified framework that adaptively integrates ItemCF with multiple diffusion models (MD, HHP, BHC, BD) via a learnable parameter ε. Unlike prior linear ensembles, HI employs nonlinear weighting to preserve complementary strengths across paradigms, enabling simultaneous optimization of recommendation quality and novelty under both sparse and dense data regimes. Evaluated on benchmark datasets (e.g., MovieLens), HI achieves consistent improvements: F1-score gains of 0.8–5.2%, up to 18.6% higher Diversity@20, and markedly enhanced robustness and novelty in sparse settings.

0 citationsRead paper