10 citations · 26 across the 30 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents
Wonjoong Kim, Sangwu Park, Yeonjun In +3
Although recent tool-augmented benchmarks involve complex requests, evaluation remains limited to answer matching, neglecting critical trajectory aspects like efficiency, hallucina…
R1-ACT: Efficient Reasoning Model Safety Alignment by Activating Safety Knowledge
Yeonjun In, Wonjoong Kim, Sangwu Park +1
Although large reasoning models (LRMs) have demonstrated impressive capabilities on complex tasks, recent studies reveal that these models frequently fulfill harmful user instructi…