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Evolve Together, Fall into Place: How Social Scaffolding Shapes Self-Evolving Multi-Agent Systems
Seojin Hwang, Youngmin Choi, Hwanhee Lee
Recent work lets LLM agents evolve their own harness, up to the team itself. Yet in human groups, the same abilities achieve different outcomes under different social conditions. B…
Leveraging Fine-grained Error Correction in Korean Speech Recognition for Consultation Services
Yonghyun Jun, Jimin Lee, Hwan Chang +3
Automatic Speech Recognition (ASR) technology is fundamental to customer service automation and large-scale transcription. However, even advanced ASR models exhibit inevitable erro…
Quantifying and Mitigating Korean Jamo-Level Typographical Vulnerabilities in Large Language Models
Seojin Lee, Hwanhee Lee
Korean introduces an additional typographical perturbation level not captured by ordinary character-level edit models: because syllable blocks are internally composed of sub-charac…
Beyond One Path: Evaluating and Enhancing Divergent Thinking in Interactive LLM Agents
Jihyeong Park, Ingeol Baek, Jeonghyun Park +1
Divergent thinking is a core dimension of creativity, yet existing evaluations of Large Language Models (LLMs) treat them as single-turn text generations, failing to capture how an…
Framing Matters: Addressing Framing Sensitivity in Decision-Making through Behaviorally-Grounded Value Alignment
Seojin Hwang, Minju Kim, Junhyuk Choi +2
Large Language Models (LLMs) are increasingly deployed in high-stakes decision-making settings such as legal reasoning, where consistency under factually equivalent inputs is criti…
Mitigating Cognitive Inertia in Large Reasoning Models via Latent Spike Steering
Seojin Lee, ByeongJeong Kim, Hwanhee Lee
While Large Reasoning Models (LRMs) have achieved remarkable performance by scaling test-time compute, they frequently suffer from Cognitive Inertia, a failure pattern manifesting…
Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion
Jeonghyun Park, Byeongjeong Kim, Seojin Hwang +1
Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, resulting in the widespread adopt…