DeepSeek-R1 Reasoning Tokens Leak in LM Studio

The "reasoning model" era is hitting a usability wall in local LLM interfaces as raw Chain-of-Thought (CoT) tokens leak into user conversations. DeepSeek-R1 and its distilled variants require specific Jinja template support in the UI to correctly hide or format the internal reasoning phase. The user's hardware (RTX 5070ti, 32GB RAM) is more than sufficient, confirming that the issue is a software/configuration failure rather than a resource bottleneck. Reasoning models can "hallucinate" technical jargon if prompted without clear grounding or if the context window is corrupted by raw CoT history. Local LLM UIs like LM Studio need better automated detection of reasoning models to apply "thinking" UI blocks by default, improving the experience for casual users. This user frustration underscores that the gap between a "capable model" and a "usable tool" remains the primary hurdle for local AI adoption.