The IndiaAI–NCG CATCH Grant for Cancer 2026 compendium documents ten winning AI solutions selected through a competitive grant program jointly run by IndiaAI (MeitY) and the National Cancer Grid's Koita Centre for Digital Oncology. It profiles AI tools spanning cancer screening, diagnostics, clinical decision support, patient engagement, and operational efficiency, ranging from pilot-ready to commercially deployed systems. Each solution is evaluated against criteria including clinical relevance, technical maturity, data governance, DPDP compliance, and deployment feasibility within Indian hospital settings. The compendium also covers the selection methodology, jury composition, responsible AI principles, and key learnings from the challenge, serving as a reference for hospitals, policymakers, and innovators seeking to adopt or scale AI-enabled cancer care across India.

Contrastive learning has become the dominant paradigm for aligning heterogeneous modalities in multimodal large language models (MLLMs). Information Noise Contrastive Estimation (InfoNCE) Loss and its variants serve as the foundational training objectives for such models. As models scale to billions of parameters and training batches reach millions of samples, the limitations of conventional softmax-based contrastive losses have become increasingly apparent: quadratic memory growth, batch size sensitivity, semantic noise in negative sampling, and the inherent tension between alignment and uniformity. This paper presents a comprehensive survey of the evolution from InfoNCE to next-generation objectives for multimodal LLMs. We analyze the theoretical foundations of contrastive learning through the alignment-uniformity framework, examine the practical innovations that have enabled unprecedented batch scaling, and evaluate emerging alternatives including sigmoid-based losses, non-contrastive alignment methods, and hybrid generative-discriminative objectives.

Why owning both the silicon and the software is the only durable moat in the age of AI, and why the margin arithmetic of fragmented supply chains is finally breaking down. By Dhritiman Mallick · Founder & CEO, Vyuhaa Med Data

The article explores how China is aggressively advancing its geopolitical influence by releasing powerful, open-source AI models, making high-tier AI capabilities freely and globally accessible. Contrasts this with the proprietary, closed-source approaches dominated by US tech giants. The piece analyzes India's strategic position within this shifting, multipolar AI ecosystem, arguing that India must carefully balance its infrastructure dependencies, leverage open-source breakthroughs for localized economic growth, and craft its own independent AI policies to avoid being caught in a digital duopoly between the US and China.

This article highlights how AI-powered image validation in PCSAP is improving India’s grain procurement monitoring system. With thousands of procurement centers and nearly 14 lakh photographs uploaded every season, manual verification had become difficult and time-consuming. The AI system validates images in real time by checking location, timestamp, quality, duplication, and content relevance. This helps improve transparency, reduce manual effort, enhance data accuracy, and support faster decision-making in procurement center assessments. The solution has been piloted in Punjab, Haryana, and Odisha, with potential for wider use in government monitoring and compliance programs.