Institution profile

Yonsei University

Academic institutionasia · kr
Official website
Research library904linked papers
Opportunities0open roles
Selected work

Representative Papers

Design of Seamless Multi-modal Interaction Framework for Intelligent Virtual Agents in Wearable Mixed Reality Environment

Jul 01, 2019International Conference on Computer Animation and Social Agents

To address high interaction latency, modality fragmentation, and weak immersion in wearable mixed reality (MR) environments, this paper proposes a lightweight multimodal intelligent agent framework. Methodologically, it integrates spatial mapping, automatic speech recognition (ASR), gaze estimation, object detection, and knowledge-graph-driven dialogue, underpinned by a cloud-edge collaborative computing architecture for efficient computational offloading; it further introduces novel mechanisms for automatic speech–animation synchronization and human-like gaze modeling. The key contributions are: (1) the first realization of low-latency (2–4 seconds), high-naturalness virtual–physical interaction on resource-constrained edge devices; (2) a modular, cross-device-compatible framework supporting all SLAM-capable MR headsets. Evaluation in real-world museum and botanical garden deployments demonstrates significant improvements in user engagement and content retention rates.

22 citationsRead paper

ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions

Oct 02, 2023arXiv.org

Decision-making under high uncertainty and limited domain expertise—such as selecting a PhD program or specialized equipment—poses significant cognitive challenges due to information overload, ambiguous criteria, and insufficient contextual grounding. Method: This paper introduces a user-centric multi-LLM agent dialogue system that pioneers “user-driven orchestration”: a collaborative paradigm where users actively coordinate agents rather than delegating tasks. The system integrates multi-role agent architecture, user-centered interaction protocols, dynamic scheduling, and context-aware feedback generation to enable synchronous exploration of diverse perspectives, real-time interrogation, and co-construction of personalized evaluation criteria. Contribution/Results: A user study (n=12) demonstrates that the system significantly outperforms web search and commercial multi-agent tools in decision confidence, satisfaction, contextual understanding, and decision quality. It further achieves strong controllability, interpretability, and adaptive decision companionship—bridging the gap between automation and human agency in high-stakes, knowledge-sparse domains.

9 citations2 influentialRead paper

SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation

Aug 16, 2024arXiv.org

In generative recommendation, inconsistent outputs for identical user histories arise from discrepancies between prompt templates and item indexing schemes, limiting sequential recommendation performance. To address this, we propose a generative retrieval framework that jointly incorporates heterogeneous item indexing and multi-template prompting to leverage large language models (LLMs) for candidate generation. We further introduce the first self-consistency–based re-ranking mechanism for generative recommendation, which jointly models dual-path preferences—textual semantics and collaborative signals—via voting and confidence-weighted aggregation over multi-source LLM generations. Evaluated on three real-world datasets, our method significantly outperforms state-of-the-art approaches, achieving up to a 12.7% improvement in Recall@10. This work marks the first successful integration and trustworthy ranking of multi-source heterogeneous knowledge—spanning semantic and collaborative modalities—within a generative recommendation paradigm.

6 citations2 influentialRead paper

SpectrumFM: A Foundation Model for Intelligent Spectrum Management

May 02, 2025arXiv.org

To address the low recognition accuracy, slow convergence, and poor generalization of existing small-scale models in dynamic spectrum environments, this paper proposes SpectrumFM—a spectral foundation model. Methodologically, SpectrumFM integrates CNNs with multi-head self-attention to enhance IQ-signal representation learning; introduces the first foundation-model paradigm for spectrum analysis, featuring dual self-supervised pretraining tasks—masked signal reconstruction and next-time-step signal prediction; and employs parameter-efficient fine-tuning (e.g., LoRA) for cross-task transfer. Experiments demonstrate significant improvements: 12.1% higher accuracy in automatic modulation classification (AMC), 9.3% gain in wireless technology classification (WTC), an AUC of 0.97 for spectrum sensing at −4 dB SNR, over 10% improvement in anomaly detection performance, faster convergence, and markedly enhanced few-shot adaptation capability.

3 citations1 influentialRead paper

Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erd\H{o}s Problems

Jan 29, 2026

This work proposes a semi-autonomous discovery framework that integrates artificial intelligence with human expertise to investigate 700 mathematical conjectures labeled as “open” in Bloom’s Erdős Problem Database. Leveraging the Gemini large language model for natural language reasoning and automated literature comparison as an initial screening step, candidate solutions are subsequently evaluated by domain experts for correctness and novelty. The study reveals that many problems deemed “open” stem not from intrinsic difficulty but from challenges in literature retrieval—termed “information occlusion.” Among the 13 problems successfully resolved, five yielded novel AI-generated solutions, while eight were traced to previously published results. This research represents the first large-scale demonstration of human–AI collaborative verification in mathematical conjectures and highlights the risk of “unconscious plagiarism” inherent in AI-assisted scholarly discovery.

2 citationsRead paper
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