Selected work

Representative Papers

Persistent Depth Ordering amid Shifting Block-Bypass Responses in Language Model Pretraining

Oct 01, 2026

This study addresses whether layer intervention responses during language model pretraining reflect persistent organizational structures or transient training effects. To investigate this, the work employs single-block identity bypasses and fixed teacher-forced contexts to conduct a longitudinal comparative analysis across multiple checkpoint trajectories. The findings reveal that the depth-wise ranking of layer sensitivity remains persistently stable throughout training, whereas intervention magnitudes are dynamically redistributed. Furthermore, the balancing mechanism between local missing updates and downstream responses varies across models. These results demonstrate that longitudinal layer sensitivity is structured yet non-static, thereby challenging the generalizability of intervention-based conclusions drawn from single checkpoints.

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Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data

Sep 25, 2026

This study addresses the challenge of acquiring high-speed dynamic badminton skills in humanoid robots due to the scarcity of human motion data. To this end, we propose a three-stage hierarchical reinforcement learning framework that pioneers the integration of task-randomized motion augmentation with context-conditioned adversarial regularization. By modeling a latent skill space, our approach generates diverse hitting motions from limited data and composes them online during execution, effectively balancing movement naturalness with task performance. This work demonstrates, for the first time on a physical humanoid robot, multi-skill human-robot rallies encompassing forehand strokes, backhand strokes, and highly dynamic jumping returns.

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Poly-SVC: Polyphony-Aware Singing Voice Conversion with Harmonic Modeling

May 12, 2026

This work addresses the challenge that existing singing voice conversion (SVC) methods struggle to reliably extract clean vocal melodies from accompanied recordings due to harmonic interference. To overcome this limitation, we propose a zero-shot, cross-lingual SVC system that explicitly models both the main melody and residual harmonics—a first in SVC—enabling effective processing of polyphonic audio. The architecture integrates a CQT-based pitch extractor, a stochastic sampler, and a conditional flow-matching diffusion decoder, jointly optimizing pitch, linguistic content, and time–frequency features. Experimental results demonstrate that our approach consistently outperforms current baselines on both harmonically rich and monophonic datasets, achieving superior performance in terms of naturalness, timbre similarity, and harmonic reconstruction fidelity.

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LLM-YOLOMS: Large Language Model-based Semantic Interpretation and Fault Diagnosis for Wind Turbine Components

Nov 13, 2025

To address the lack of semantic interpretability in visual inspection results for wind turbine (WT) component fault diagnosis—hindering operational and maintenance decision-making—this paper proposes a vision–language collaborative diagnostic framework. First, YOLOMS with multi-scale detection and sliding-window cropping enhances small-object recognition accuracy. Second, a lightweight key-value (KV) mapping module automatically converts structured detection outputs—including bounding box coordinates, class labels, and confidence scores—into qualitative and quantitative natural-language descriptions. Third, a domain-adapted large language model (LLM) performs semantic reasoning to generate human-understandable fault analyses and actionable maintenance recommendations. Evaluated on a real-world dataset, the framework achieves 90.6% fault detection accuracy and 89% accuracy in generating maintenance reports, significantly improving both the interpretability and engineering applicability of diagnostic outcomes.

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MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture

Oct 17, 2025

Global healthcare infrastructure is severely unevenly distributed, with remote and underserved regions lacking access to basic medical services; conventional planning methods fail to address the scale and urgency of this challenge. This study proposes an agent-based hybrid intelligence framework that integrates large language models (LLMs) with deterministic rule engines, implementing a tri-agent collaborative system—comprising demand elicitation, rule-based inference, and 3D generative design—to enable natural-language health-need articulation, automated functional layout translation, and climate- and resource-constrained adaptation in a closed-loop design workflow. A lightweight web platform, deployed via satellite internet, supports real-time, multilingual solution generation under low-bandwidth conditions. Empirical evaluation demonstrates that the system generates code-compliant healthcare facility designs within minutes, reducing preliminary planning time by over 80%. Feasibility and improved accessibility have been validated across multiple low-resource settings.

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Recent publications

Latest Papers

Persistent Depth Ordering amid Shifting Block-Bypass Responses in Language Model Pretraining

Oct 01, 2026

This study addresses whether layer intervention responses during language model pretraining reflect persistent organizational structures or transient training effects. To investigate this, the work employs single-block identity bypasses and fixed teacher-forced contexts to conduct a longitudinal comparative analysis across multiple checkpoint trajectories. The findings reveal that the depth-wise ranking of layer sensitivity remains persistently stable throughout training, whereas intervention magnitudes are dynamically redistributed. Furthermore, the balancing mechanism between local missing updates and downstream responses varies across models. These results demonstrate that longitudinal layer sensitivity is structured yet non-static, thereby challenging the generalizability of intervention-based conclusions drawn from single checkpoints.

0 citationsRead paper

Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data

Sep 25, 2026

This study addresses the challenge of acquiring high-speed dynamic badminton skills in humanoid robots due to the scarcity of human motion data. To this end, we propose a three-stage hierarchical reinforcement learning framework that pioneers the integration of task-randomized motion augmentation with context-conditioned adversarial regularization. By modeling a latent skill space, our approach generates diverse hitting motions from limited data and composes them online during execution, effectively balancing movement naturalness with task performance. This work demonstrates, for the first time on a physical humanoid robot, multi-skill human-robot rallies encompassing forehand strokes, backhand strokes, and highly dynamic jumping returns.

0 citationsRead paper

Poly-SVC: Polyphony-Aware Singing Voice Conversion with Harmonic Modeling

May 12, 2026

This work addresses the challenge that existing singing voice conversion (SVC) methods struggle to reliably extract clean vocal melodies from accompanied recordings due to harmonic interference. To overcome this limitation, we propose a zero-shot, cross-lingual SVC system that explicitly models both the main melody and residual harmonics—a first in SVC—enabling effective processing of polyphonic audio. The architecture integrates a CQT-based pitch extractor, a stochastic sampler, and a conditional flow-matching diffusion decoder, jointly optimizing pitch, linguistic content, and time–frequency features. Experimental results demonstrate that our approach consistently outperforms current baselines on both harmonically rich and monophonic datasets, achieving superior performance in terms of naturalness, timbre similarity, and harmonic reconstruction fidelity.

0 citationsRead paper

LLM-YOLOMS: Large Language Model-based Semantic Interpretation and Fault Diagnosis for Wind Turbine Components

Nov 13, 2025

To address the lack of semantic interpretability in visual inspection results for wind turbine (WT) component fault diagnosis—hindering operational and maintenance decision-making—this paper proposes a vision–language collaborative diagnostic framework. First, YOLOMS with multi-scale detection and sliding-window cropping enhances small-object recognition accuracy. Second, a lightweight key-value (KV) mapping module automatically converts structured detection outputs—including bounding box coordinates, class labels, and confidence scores—into qualitative and quantitative natural-language descriptions. Third, a domain-adapted large language model (LLM) performs semantic reasoning to generate human-understandable fault analyses and actionable maintenance recommendations. Evaluated on a real-world dataset, the framework achieves 90.6% fault detection accuracy and 89% accuracy in generating maintenance reports, significantly improving both the interpretability and engineering applicability of diagnostic outcomes.

0 citationsRead paper

MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture

Oct 17, 2025

Global healthcare infrastructure is severely unevenly distributed, with remote and underserved regions lacking access to basic medical services; conventional planning methods fail to address the scale and urgency of this challenge. This study proposes an agent-based hybrid intelligence framework that integrates large language models (LLMs) with deterministic rule engines, implementing a tri-agent collaborative system—comprising demand elicitation, rule-based inference, and 3D generative design—to enable natural-language health-need articulation, automated functional layout translation, and climate- and resource-constrained adaptation in a closed-loop design workflow. A lightweight web platform, deployed via satellite internet, supports real-time, multilingual solution generation under low-bandwidth conditions. Empirical evaluation demonstrates that the system generates code-compliant healthcare facility designs within minutes, reducing preliminary planning time by over 80%. Feasibility and improved accessibility have been validated across multiple low-resource settings.

0 citationsRead paper