Although behavioral studies have documented numerical reasoning errors in large language models (LLMs), the underlying representational mechanisms remain unclear. We hypothesize that numerical attributes occupy shared latent subspaces and investigate two questions: (1) How do LLMs internally integrate multiple numerical attributes of a single entity? (2) How does irrelevant numerical context perturb these representations and their downstream outputs? To address these questions, we combine linear probing with partial correlation analysis and prompt-based vulnerability tests across models of varying sizes. Our results show that LLMs encode real-world numerical correlations but tend to systematically amplify them. Moreover, irrelevant context induces consistent shifts in magnitude representations, with downstream effects that vary by model size. These findings reveal a vulnerability in LLM decision-making and lay the groundwork for fairer, representation-aware control under multi-attribute entanglement.

We present **JPharmatron**, a Japanese domain-specific large language model (LLM) for the pharmaceutical field, developed through continual pre-training on two billion Japanese pharmaceutical tokens and eight billion English biomedical tokens. For rigorous evaluation, we introduce **JPharmaBench**, a benchmark suite consisting of three new benchmarks: YakugakuQA, based on national pharmacist licensing exams; NayoseQA, which tests cross-lingual synonym and terminology normalization; and SogoCheck, a novel task involving cross-document consistency checking.We evaluate our model against open-source medical LLMs and commercial models, including GPT-4o. Experimental results show that **JPharmatron** outperforms existing open models and achieves competitive performance with commercial ones.Interestingly, even GPT-4o performs poorly on SogoCheck, suggesting that cross-sentence consistency reasoning remains an open challenge.**JPharmatron** enables secure and local model deployment for pharmaceutical tasks, where privacy and legal constraints limit the use of closed models. Besides, **JPharmaBench** offers a reproducible framework for evaluating Japanese pharmaceutical natural language processing. Together, they demonstrate the feasibility of practical and cost-efficient language models for Japanese healthcare and pharmaceutical sectors.Our model, codes, and datasets are available on HuggingFace: https://huggingface.co/collections/EQUES/jpharmatron and https://huggingface.co/collections/EQUES/jpharmabench.