We present the Barcelona Supercomputing Center (BSC) submission to the Instruction Following (IF) track of IWSLT 2026, which evaluates unified spoken language systems capable of solving multiple tasks through natural language instructions. Our system consists of an end-to-end (E2E) architecture that combines a speech encoder with a translation-oriented Large Language Model. The model is trained on speech and text data, covering automatic speech recognition, translation, question answering, and instruction following. We investigate a Chain-of-Thought (CoT) generation strategy that explicitly decomposes tasks by producing an intermediate transcription before the final output, which enables effective reuse of text-only supervision and improves robustness across tasks. To further support generalization, we design diverse prompt formulations and align text-only and speech inputs under a shared inference pattern. Results on IWSLT 2025 evaluation data show that our approach achieves competitive and even state-of-the-art performance across tasks.

Rapid advancements in Visual Language Models (VLMs) have transformed multimodal understanding but are often constrained by generating English responses regardless of the input language. This phenomenon has been termed as Image-induced Fidelity Loss (IFL) and stems from limited multimodal multilingual training data. To address this, we propose a continuous multilingual integration strategy that injects text-only multilingual data during visual instruction tuning, preserving the language model’s original multilingual capabilities. Extensive evaluations demonstrate that our approach significantly improves linguistic fidelity across languages without degradation in visual performance. We also explore model merging, which improves language fidelity but comes at the cost of visual performance. In contrast, our core method achieves robust multilingual alignment without trade-offs, offering a scalable and effective path to mitigating IFL for global VLM adoption.