Dohyeon Lee
I am a Ph.D. Student at the LDI Lab of Seoul National University.
I am fortunate to be advised by Prof. Seung-won Hwang.
My research interests focus on agentic AI on information retrieval system.
Research Area
An Agentic Retrieval System is a retrieval framework in which agents actively interact with their environment to adapt, make decisions, and improve performance over time. The notion of agentic behavior emphasizes that agents are not static components but autonomous actors capable of exploration and adaptation. The environment can take many forms—including data, tools, memory, and other agents—which shape how the agent perceives, learns, and acts within the retrieval ecosystem.How Agentic Retrieval System Interact with Data
Agents are exposed to new domain-specific corpora and tasked with adapting their retrieval strategies accordingly. Through exploration, they identify informative patterns or signals—such as domain-specific term distributions or statistical cues like inverse document frequency (IDF)—which help refine queries and relevance assessments. This interaction enables adaptive retrieval in unfamiliar or evolving data environments.
How Agentic Retrieval System Interact with Other Agents
In multi-agent retrieval settings, each agent may specialize in a particular domain, retrieval strategy, or reasoning style. Effective collaboration depends on defining clear roles and encouraging complementary behaviors that enhance overall system performance. A key research focus is on quantifying and optimizing synergy among agents, such as minimizing redundancy or maximizing coverage across retrieved information.
How Agentic Retrieval System Interact with Tools
Agents interact with a variety of retrieval tools—such as query reformulators, rerankers, or multiple retrieval backends—and learn how to use them effectively. This includes not only deciding which tools to invoke but also determining what input to provide and how to interpret the output for downstream decision-making. In this view, agents serve as policy models that orchestrate tool usage based on context and task requirements.
How Agentic Retrieval System Interact with Memory
Agents benefit from storing and recalling useful patterns, strategies, or outcomes from past retrieval experiences. This memory may take the form of episodic traces, cached retrieval states, or learned priors that inform future decisions. A central challenge lies in determining what to remember, how to represent it, and when to leverage it to improve ongoing retrieval behavior.
Publications
From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval
Arxiv 2025
tRAG: Term-level Retrieval-Augmented Generation for Zero-shot Retrieval
NAACL 2025
DADA: Distribution-Aware Domain Adaptation of PLMs for Information Retrieval
Findings of ACL 2024
HIL: Hybrid Isotropy Learning for Zero-shot Performance in Dense retrieval
NAACL 2024
Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding
EMNLP 2024
C2LIR: Continual Cross-lingual Transfer for Low-Resource Information Retrieval
ECIR 2023 (short)
Script, Language, and Labels: Overcoming Three Discrepancies for Low-Resource Language Specialization
AAAI 2023
SCOPA - Soft Code-Switching and Pairwise Alignment for Zero-Shot Cross-lingual Transfer
CIKM 2021 (short)
Education
Seoul National University - Ph.D. in Computer Science and Engineering
2021 - (In progress)Advisor: Prof. Seung-won Hwang
Doctoral disseration: (TBD)
Yonsei University - M.S. in Computer Science
2019 - 2021Advisor: Prof. Seung-won Hwang
Master's thesis: Orthogonal Disentanglement of Semantic and Symbolic Representation for Query-Document Matching
Yonsei University - B.S. in Computer Science
2015 - 2019GPA: 4.06 / 4.5
Awards & Scholarships
BK21+ Outstanding Research Fellowship
2023Korean Government Scholarship Program
Computer Science Department Scholarship
2019 - 2021at Yonsei University (graduate school)
Computer Science Department Scholarship
2017at Yonsei University (undergraduate)
Experience
NAVER Corp. - Research Intern
2020.3 - 2020.6Research title: Sparse-Dense Hybrid Retrieval
Mentor: Ph.D. Sunghyun Park