4 citations · 4 across the 11 of their papers we have counts for
18 papers
Naive Visual Memory is Not Enough: A Failure-Mode Study of GUI Agents
Seoyoung Choi, Minseok Ko, Hyunseok Lee +4
Graphical User Interface (GUI) agents are increasingly used to automate complex computer tasks across applications, websites, and operating systems. To improve their reliability, r…
IdleSpec: Exploiting Idle Time via Speculative Planning for LLM Agents
Daewon Choi, Kyunghyun Park, Woomin Song +4
Large language model (LLM)-based agents solve complex tasks by leveraging multi-step reasoning with iterative tool calls and environment interactions, which incur idle time while w…
ExComm: Exploration-Stage Communication for Error-Resilient Agentic Test-Time Scaling
Woomin Song, Beomjun Kim, Daewon Choi +4
A common failure mode in long-horizon agentic test-time scaling is error propagation, where factual errors or invalid deductions introduced at intermediate steps persist in the age…
RedacBench: Can AI Erase Your Secrets?
Hyunjun Jeon, Kyuyoung Kim, Jinwoo Shin
Modern language models can readily extract sensitive information from unstructured text, making redaction -- the selective removal of such information -- critical for data security…
Beyond Correctness: Learning Robust Reasoning via Transfer
Hyunseok Lee, Soheil Abbasloo, Jihoon Tack +1
Reinforcement Learning with Verifiable Rewards (RLVR) has recently strengthened LLM reasoning, but its focus on final answer correctness leaves a critical gap: it does not ensure t…
Scalable and Robust LLM Unlearning by Correcting Responses with Retrieved Exclusions
Junbeom Kim, Kyuyoung Kim, Jihoon Tack +2
Language models trained on web-scale corpora risk memorizing and exposing sensitive information, prompting the need for effective machine unlearning. Prior methods mainly focus on…