In Korean medical LLMs, domain-trained models underperform General LLMs of comparable parameter scale. We attribute this *paradoxical failure* to a *cascading two-bottleneck* structure: tokenizer over-fragmentation (*Fragmentation bottleneck, B1*) compounded by a learning-stage representation-alignment failure (*Alignment bottleneck, B2*). Using the Korean medical domain as a case study, we diagnose this paradox and propose **VocMoE**, which is designed to address both bottlenecks: DVE selects domain vocabulary from a medical BERT via an information-theoretic score, and MoDE uses the DVE output (the partition of token ids) as the vocabulary-conditioned, deterministic routing key of the MoE FFN. In this view, the vocabulary partition itself serves as a routing prior, shifting routing from hidden-state learning to vocabulary identity. MediKo-9B-A4B reaches simultaneous SOTA on KorMedMCQA (**57.5**, +14.8 pt), KMMLU Medical, and HAE-RAE, while MediKo-4B-A1B (active 1.69B) surpasses Qwen3-8B (8B) and KULLM3 (10.7B) on KorMedMCQA, updating the active-parameter efficiency frontier.

Recent advances in Multimodal Large Language Models (MLLMs) have improved image recognition and reasoning, but video-related tasks remain challenging due to memory constraints from dense frame processing. Existing Video Moment Retrieval (VMR) methodologies rely on sparse frame sampling, risking potential information loss, especially in lengthy videos. We propose SMORE (See MORE, store less), a framework that enhances memory efficiency while maintaining high information resolution. SMORE (1) uses query-guided captions to encode semantics aligned with user intent, (2) applies query-aware importance modulation to highlight relevant segments, and (3) adaptively compresses frames to preserve key content while reducing redundancy. This enables efficient video understanding without exceeding memory budgets. Experimental validation reveals that SMORE achieves state-of-the-art performance on QVHighlights, Charades-STA, and ActivityNet-Captions benchmarks.

Vision language models (VLMs) demonstrate strong zero-shot performance, but often perpetuate social stereotypes in person-centric queries, yielding skewed demographic distributions. Current debiasing methods apply uniform bias corrections across all input queries regardless of their bias sensitivity, creating a fundamental fairness–utility trade-off. Strong debiasing distorts semantically meaningful information in bias-insensitive queries, while weak debiasing fails to mitigate stereotypes in bias-sensitive ones. This one-size-fits-all approach hampers simultaneously achieving high utility on bias-insensitive queries and fairness on bias-sensitive queries. We introduce Reward-Gated Test-Time Adaptation (RG-TTA), a reinforcement learning-based test-time adaptation framework that selectively applies debiasing based on input sensitivity. RG-TTA adaptively triggers fairness regularization based on the bias sensitivity of each input during test-time policy adaptation, while focusing exclusively on optimizing cross-modal alignment for bias-insensitive inputs. Experiments on fairness benchmarks (e.g., FairFace, UTKFace) demonstrate substantial bias reduction while simultaneously improving zero-shot utility, resolving the trade-off of uniform debiasing.