HinTel-AlignBench: A Framework and Benchmark for Hindi-Telugu with English-Aligned Samples

Emversity, an Indian startup focused on job-ready training, has successfully raised $30 million in a new funding round, doubling its valuation as it aims to scale its operations in a market increasingly focused on skills that artificial intelligence cannot replace.

Researchers at the University of Surrey have developed the world's first dataset designed to enhance English-to-Malayalam machine translation, addressing a significant gap for this low-resource language spoken by over 38 million people in India.

A recent study emphasizes the importance of data curation in machine translation, particularly for low-resource languages. The research introduces LALITA, a framework designed to optimize the selection of source sentences for creating parallel corpora, focusing on English-Hindi bi-text to enhance machine translation performance.

The introduction of VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark, aims to assess the capabilities of vision-language models (VLMs) in interpreting and reasoning over visual and textual information in Vietnamese. This benchmark includes 2.5k multimodal questions across seven diverse tasks, emphasizing genuine multimodal integration rather than text-only cues.

A new dataset named IndRegBias has been introduced to study regional biases in English and code-mixed comments on social media platforms like Reddit and YouTube, focusing on Indian contexts. This dataset comprises 25,000 comments that reflect regional biases, which have been less explored compared to other social biases such as gender and race.

A new study presents an Edge-AI perception node designed for real-time traffic violation analytics and safety event dissemination in India, addressing the challenges posed by rapid motorization and a significant enforcement gap, with over 11 million violations recorded in 2023.

A new approach called SubTTA has been proposed to enhance test-time adaptation (TTA) for Vision-Language Models (VLMs), addressing vulnerabilities to distribution shifts that can misguide adaptation through unreliable zero-shot predictions. SubTTA aligns the semantic subspaces of visual and textual modalities to improve the accuracy of predictions during adaptation.

A new framework named R^4 has been proposed to enhance medical image analysis by integrating Vision-Language Models (VLMs) into a multi-agent system that includes a Router, Retriever, Reflector, and Repairer, specifically focusing on chest X-ray analysis. This approach aims to improve reasoning, safety, and spatial grounding in medical imaging workflows.