7 citations · 15 across the 16 of their papers we have counts for
16 papers
MVTrack4Gen: Multi-View Point Tracking as Geometric Supervision for 4D Video Generation
JoungBin Lee, Jaewoo Jung, Jongmin Lee +8
Synthesizing a novel-view video from a monocular reference video along a target camera trajectory requires both geometric consistency and motion fidelity with respect to the refere…
Multi-Agent Transactive Memory
To Eun Kim, Xuhong He, Dishank Jain +3
Decentralized deployment of LLM agents with diverse capabilities motivates infrastructure for knowledge sharing across heterogeneous agent populations. Just as search engines index…
Multilingual and Domain-Agnostic Tip-of-the-Tongue Query Generation for Simulated Evaluation
Xuhong He, To Eun Kim, Maik Fröbe +3
Tip-of-the-Tongue (ToT) retrieval benchmarks have largely focused on English, limiting their applicability to multilingual information access. In this work, we construct multilingu…
Evaluation of Agents under Simulated AI Marketplace Dynamics
To Eun Kim, Alireza Salemi, Hamed Zamani +1
Modern information access ecosystems consist of mixtures of systems, such as retrieval systems and large language models, and increasingly rely on marketplaces to mediate access to…
CORAL: Correspondence Alignment for Improved Virtual Try-On
Jiyoung Kim, Youngjin Shin, Siyoon Jin +6
Existing methods for Virtual Try-On (VTON) often struggle to preserve fine garment details, especially in unpaired settings where accurate person-garment correspondence is required…
Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities
Changdae Oh, Seongheon Park, To Eun Kim +8
Uncertainty quantification (UQ) for large language models (LLMs) is a key building block for safety guardrails of daily LLM applications. Yet, even as LLM agents are increasingly d…
Overview of the TREC 2025 Tip-of-the-Tongue track
Jaime Arguello, Fernando Diaz, Maik Fröebe +2
Tip-of-the-tongue (ToT) known-item retrieval involves re-finding an item for which the searcher does not reliably recall an identifier. ToT information requests (or queries) are ve…
TeamCMU at Touché: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search
To Eun Kim, João Coelho, Gbemileke Onilude +1
As conversational search engines increasingly adopt generation-based paradigms powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), the integration of…
MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Jushaan Singh Kalra, Xinran Zhao, To Eun Kim +3
Retrieval-augmented Generation (RAG) is powerful, but its effectiveness hinges on which retrievers we use and how. Different retrievers offer distinct, often complementary signals:…
LTRR: Learning To Rank Retrievers for LLMs
To Eun Kim, Fernando Diaz
Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs optimally across all query types…
Tip of the Tongue Query Elicitation for Simulated Evaluation
Yifan He, To Eun Kim, Fernando Diaz +2
Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. While common, existing search systems often fail to effective…
Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation
To Eun Kim, Fernando Diaz
Despite the central role of retrieval in retrieval-augmented generation (RAG) systems, much of the existing research on RAG overlooks the well-established field of fair ranking and…
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
To Eun Kim, Alireza Salemi, Andrew Drozdov +2
In the field of language modeling, models augmented with retrieval components have emerged as a promising solution to address several challenges faced in the natural language proce…
A Comprehensive Survey on Generative Diffusion Models for Structured Data
Heejoon Koo, To Eun Kim
In recent years, generative diffusion models have achieved a rapid paradigm shift in deep generative models by showing groundbreaking performance across various applications. Meanw…
When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solution
Zhengxiang Shi, Jerome Ramos, To Eun Kim +3
In collaborative tasks, effective communication is crucial for achieving joint goals. One such task is collaborative building where builders must communicate with each other to con…
Attention-based Ingredient Phrase Parser
Zhengxiang Shi, Pin Ni, Meihui Wang +2
As virtual personal assistants have now penetrated the consumer market, with products such as Siri and Alexa, the research community has produced several works on task-oriented dia…