6 papers
Self-Assessment Reinforcement Learning for Search-State Awareness in Retrieval-Augmented Large Language Models
Taewhoo Lee, Minju Song, Jungwoo Park +6
Recent advances in search agents have enabled large language models to tackle complex tasks by retrieving and synthesizing external information. However, existing search agents oft…
Reason Through the Latent! Making Latent Visual Reasoning Necessary
Suhyeong Park, Junha Jung, Jaewoo Kang
Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textual chains of thought. However, visual information being pres…
From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b
Taeyun Roh, Eunha Lee, Wonjune Jang +3
Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents. T…
Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval
Suhyeong Park, Junha Jung, Jungwoo Park +1
Multi-vector vision-language retrieval preserves fine-grained visual evidence through maximum-similarity late interaction, but dense image-side tokens make storage and scoring expe…
Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning
Junha Jung, Minbyul Jeong, Suhyeon Lim +5
Recent multimodal large language models have shown great promise in clinical image reasoning, but existing post-training pipelines remain predominantly outcome-centric, relying on…
CLAG: Adaptive Memory Organization via Agent-Driven Clustering for Small Language Model Agents
Taeyun Roh, Wonjune Jang, Junha Jung +1
Large language model agents heavily rely on external memory to support knowledge reuse and complex reasoning tasks. Yet most memory systems store experiences in a single global ret…