1 citations · 2 across the 8 of their papers we have counts for
11 papers
LEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents
Qingni Wang, Changdae Oh, Yuzhe Yang +4
Computer-use agents increasingly tackle long-horizon tasks, where running a frontier model at every step is expensive and a small open-weight model alone often fails. Weak-to-stron…
Graph Sensitivity of Cartesian Products with Matched Bridges
Zhen-Mu Hong, Zi-Yi Wu, Zheng-Jiang Xia
For a graph , let denote the minimum of the maximum degree of an induced subgraph with vertices, where is the independence number, and write $f(G)=f_1(G…
Sharpening Tax in Post-Training
Changdae Oh, Qi Zeng, Qi Qi +7
An emerging hypothesis about reinforcement learning (RL) post-training of large language models (LLMs) is that it merely sharpens existing behaviors of a base model, improving sing…
Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
Changdae Oh, Wendi Li, Seongheon Park +3
Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irrever…
Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
Seongheon Park, Wendi Li, Changdae Oh +4
Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that…
Thinking Is Not Telling: Information Disclosure in User-Service LLM Agents
Jiatong Li, Changdae Oh, Hyeong Kyu Choi +2
User-engaged LLM agents increasingly operate in service scenarios where task success depends on coordination between the agent, the user, and a stateful environment. In such intera…
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…
How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability
Shawn Im, Changdae Oh, Zhen Fang +1
Semantic associations such as the link between "bird" and "flew" are foundational for language modeling as they enable models to go beyond memorization and instead generalize and g…
General Exploratory Bonus for Optimistic Exploration in RLHF
Wendi Li, Changdae Oh, Sharon Li
Optimistic exploration is central to improving sample efficiency in reinforcement learning with human feedback, yet existing exploratory bonus methods to incentivize exploration of…
Understanding Language Prior of LVLMs by Contrasting Chain-of-Embedding
Lin Long, Changdae Oh, Seongheon Park +1
Large vision-language models (LVLMs) achieve strong performance on multimodal tasks, yet they often default to their language prior (LP) -- memorized textual patterns from pre-trai…
Enhancing Temporal Action Localization: Advanced S6 Modeling with Recurrent Mechanism
Sangyoun Lee, Juho Jung, Changdae Oh +1
Temporal Action Localization (TAL) is a critical task in video analysis, identifying precise start and end times of actions. Existing methods like CNNs, RNNs, GCNs, and Transformer…