Recent advances in Multimodal Machine Translation (MMT) have attempted to address ambiguity and polysemy in text alone by enabling models to draw additional contextual cues from paired images, thereby improving disambiguation and translation accuracy.Datasets such as Multi30K and Visual Genome have significantly advanced this line of research.However, these datasets do not always compel models to rely on visual information.The CoMMuTE dataset takes a stronger step in this direction by serving as an evaluation benchmark specifically designed around ambiguous English sentences that can only be correctly interpreted with their accompanying images.In this work, we extend CoMMuTE to two Indic languages, introducing IndicCoMMuTE -an evaluation dataset for assessing MMT systems on low-resource Indic languages.We benchmark a range of open-source multimodal Large Language Models (< 15B parameters) and a strong text-only baseline across eight languages.We fine-tune one of these LLMs on two Indic languages.Our findings provide insights into the strengths and limitations of LLMs and establish IndicCoMMuTE as a valuable benchmark for future research on Multimodal Machine Translation in Indic languages.

We consider the task of quantum state certification: given a description of a hypothesis state σ and multiple copies of an unknown state ρ, a tester aims to determine whether the two states are equal or є-far in trace distance. It is known that Θ(d/є2) copies of ρ are necessary and sufficient for this task, assuming the tester can make entangled measurements over all copies. However, these bounds are for a worst-case σ, and it is not known what the optimal copy complexity is for this problem on an instance-by-instance basis. While such instance-optimal bounds have previously been shown for quantum state certification when the tester is limited to measurements unentangled across copies, they remained open when testers are unrestricted in the kind of measurements they can perform.