5 citations · 5 across the 15 of their papers we have counts for
4 papers · 1 filter
Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory
Taeil Kim, Kangsan Kim, Sung Ju Hwang
Memory systems have shown promise for improving agent performance, but their potential remains largely unexplored for small language models, which struggle to generate sufficient s…
Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR
Chanuk Lee, Sangwoo Park, Minki Kang +1
Reinforcement learning with verifiable rewards (RLVR) is a scalable paradigm for improving the mathematical reasoning of large language models, but it is fundamentally limited by e…
Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents
Kangsan Kim, Minki Kang, Taeil Kim +3
Memory-based self-evolution has emerged as a promising paradigm for coding agents. However, existing approaches typically restrict memory utilization to homogeneous task domains, f…
Multimodal Collaborative Debate for Zero-Shot Time Series Reasoning
Patara Trirat, Jin Myung Kwak, Jay Heo +2
Large language models (LLMs) are increasingly used as natural-language interfaces to structured data, yet they remain brittle when reasoning over time series. Visual patterns can b…