10 citations · 26 across the 30 of their papers we have counts for
12 papers · 1 filter
Do VLAs Really Follow Instructions? Diagnosing How-Level Instruction Following in Vision-Language-Action Models
Sangwu Park, Wonjoong Kim, Yeonjun In +1
Vision-language-action (VLA) models are expected to follow instructions like their language backbones, yet prior work has focused on What-level instructions that specify the task r…
ManiPhysicsBench: Physics-Based Assessment of Object Preservation in VLA Manipulation
Sangwu Park, Yeonjun In, Wonjoong Kim +3
Vision-language-action (VLA) models aim to perform diverse manipulation tasks, but task success in existing rigid-body benchmarks does not indicate whether they preserve objects. W…
A Behavioral Trait Leaks into Preferences: Diagnosing Trait Interference in LLM User Simulators
Chaehyun Kim, Sein Kim, Hongseok Kang +1
LLM-based user simulators aim to bridge the offline-online gap in recommender evaluation by emulating users through injected traits, where preference attributes determine what a us…
Compositional Spectral Prompts for LLM-based Online Time Series Forecasting
Seungyoon Choi, Hyunchul Kim, Jae-Gil Lee +1
To address the sequential and evolving nature of time series, the Online Time Series Forecasting (OTSF) task has been extensively studied in multiple domains. Existing research foc…
AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces
Sungho Park, Wonjoong Kim, Rongyuan Tan +10
LLM agents remain unreliable on long-horizon tasks, where small local failures can compound over extended interactions and lead to overall task failure. Although external harnesses…
Test-Time Training for Visual Foresight Vision-Language-Action Models
Sangwu Park, Wonjoong Kim, Yeonjun In +3
Visual Foresight VLA (VF-VLA) has become a prominent architectural choice in the recent VLA due to its impressive performance. Nevertheless, the inherent design of VF-VLA makes it…
Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents
Yeonjun In, Wonjoong Kim, Sangwu Park +2
Existing large language model (LLM) based memory systems apply universal, static policies that overlook a fundamental reality: the contexts that are worth storing in memory are dif…
PAIR: Prefix-Aware Internal Reward Model for Multi-Turn Agent Optimization
Wonjoong Kim, Yeonjun In, Sangwu Park +2
A significant hurdle for current LLMs is the execution of complex, multi-stage tasks. Group Relative Policy Optimization (GRPO) has been emerging as a leading choice, but its relia…
Reasoning Structure Matters for Safety Alignment of Reasoning Models
Yeonjun In, Wonjoong Kim, Sangwu Park +1
Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. This paper investigates the unde…
Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding
Jiwan Kim, Kibum Kim, Wonjoong Kim +2
Recently, visual token pruning has been studied to handle the vast number of visual tokens in Multimodal Large Language Models. However, we observe that while existing pruning meth…
Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMs
Kibum Kim, Jiwan Kim, Kyle Min +4
Video Large Language Models (Video LLMs) incur high inference latency due to a large number of visual tokens provided to LLMs. To address this, training-free visual token pruning h…
Rethinking Failure Attribution in Multi-Agent Systems: A Multi-Perspective Benchmark and Evaluation
Yeonjun In, Mehrab Tanjim, Jayakumar Subramanian +6
Failure attribution is essential for diagnosing and improving multi-agent systems (MAS), yet existing benchmarks and methods largely assume a single deterministic root cause for ea…