collaborators

7 papers

cs.AI2026

When Is Enough Not Enough? Illusory Completion in Search Agents

Dayoon Ko, Jihyuk Kim, Sohyeon Kim +5

In agentic search, an LLM agent searches the web, reads the pages it finds, and decides what to look for next before returning an answer. But can we trust an answer simply because…

cs.AI2026

Lost in the Noise: How Reasoning Models Fail with Contextual Distractors

Seongyun Lee, Yongrae Jo, Minju Seo +2

Recent advances in reasoning models and agentic AI systems have led to an increased reliance on diverse external information. However, this shift introduces input contexts that are…

cs.IR2026

Adaptive Retrieval for Reasoning-Intensive Retrieval

Jongho Kim, Jaeyoung Kim, Seung-won Hwang +3

We study leveraging adaptive retrieval to ensure sufficient "bridge" documents are retrieved for reasoning-intensive retrieval. Bridge documents are those that contribute to the re…

cs.AI2025

Hybrid Deep Searcher: Scalable Parallel and Sequential Search Reasoning

Dayoon Ko, Jihyuk Kim, Haeju Park +6

Large reasoning models (LRMs) combined with retrieval-augmented generation (RAG) have enabled deep research agents capable of multi-step reasoning with external knowledge retrieval…

cs.CL2025

Shifting from Ranking to Set Selection for Retrieval Augmented Generation

Dahyun Lee, Yongrae Jo, Haeju Park +1

Retrieval in Retrieval-Augmented Generation(RAG) must ensure that retrieved passages are not only individually relevant but also collectively form a comprehensive set. Existing app…

cs.CE2025

Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation

Chang Liu, Yixin Wang, Moontae Lee

Efficient access to high-quality information is vital for online platforms. To promote more useful information, users not only create new content but also evaluate existing content…

cs.CL2025

The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think

Seongyun Lee, Seungone Kim, Minju Seo +9

Long chain-of-thought (CoT) is an essential ingredient in effective usage of modern large language models, but our understanding of the reasoning strategies underlying these capabi…