Legal QA systems may benefit from training data that is expert-verified and associated with statutory provisions, as fluent generation alone cannot guarantee legally relevant and citation-supported outputs. However, existing Korean legal datasets provide limited support for legal QA and statute-associated response generation. To address this gap, we introduce KoLegalQA, a large-scale Korean legal question–answer corpus designed for research on legal QA and explanation-oriented legal response generation in real-world consultation scenarios. The dataset comprises 19k consultations collected from government-operated services, with all responses originally authored or verified by licensed legal professionals. Unlike prior resources, KoLegalQA provides explicit statutory references and clause-level summaries, enabling research on citation-associated and explanation-oriented legal response generation. We benchmark six Korean-capable LLMs using both automated evaluation (G-Eval) and human assessment across multiple criteria, including legal correctness, reasoning quality, and citation relevance. Experimental results show that fine-tuning on KoLegalQA generally improves legal reasoning validity and statute-associated response generation across most evaluated models. We present this resource as a practical benchmark dataset for Korean legal NLP research. Dataset splits, preprocessing scripts, and evaluation code will be publicly released to support reproducible research.

Existing artwork-oriented retrieval-augmented generation (RAG) systems typically support visual understanding through contextual information such as artist metadata and historical background. Symbolic folk-art interpretation instead requires connecting visible motifs to culturally situated meanings. We study semiotic retrieval for this task in Korean minhwa, comparing dense semantic and graph-structured retrieval. We introduce SemioticRAG, which organizes symbolic knowledge through Barthesian Form, Concept, JointConcept, and Myth structures. Blind human evaluation shows that DenseRAG performs best overall, with SemioticRAG ranking second. Qualitative analysis indicates that SemioticRAG produces more explicit pathway-based symbolic compositions, while depending on Form-level grounding and graph construction quality. Overall, explicit semiotic structure changes how culturally grounded meanings are composed rather than uniformly improving interpretive quality.

Language models (LMs) operate in evolving environments where adapting to new domains and linguistic shifts is critical. Continual pre-training (CPT) addresses this but introduces a strict stability-plasticity dilemma. While current approaches manage this via data replay or architectural changes, they largely treat the optimizer as a static component. We propose the Plasticity-Inducing Control Optimizer (PICO) to tackle this dilemma directly at the update level. The optimizer defines a utility-gated update together with a stochastic perturbation path controlled by periodic spectral sensitivity checks. Structural flags suppress the perturbation path while leaving the protected-gradient update active. Across the evaluated legal and medical streams, PICO exhibits a favorable stability–plasticity trade-off through optimizer-level control, while incurring a measurable spectral-monitoring cost. Code is available at https://github.com/infonet-gist/infonet-ai-pico.