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💬 Computation & Language

AI Struggles with Indian Culture: New Method Boosts Understanding by 20%

📄 VIRAASAT: Traversing Novel Paths for Indian Cultural Reasoning

Researchers discovered that even advanced AI models perform poorly when asked complex questions about Indian culture, history, and traditions. They created VIRAASAT, a dataset of over 3,200 challenging cultural questions spanning all Indian states, and developed a new training method called Symbolic Chain-of-Manipulation (SCoM) that teaches AI to think step-by-step through cultural knowledge like navigating a map. This approach improved AI performance by up to 20% compared to standard methods, helping models better understand and reason about diverse cultural contexts.

Feb 20, 2026 View on arXiv →
👁️ Computer Vision

Meta Trains Massive AI Vision Model on 25B Social Media Posts

📄 Xray-Visual Models: Scaling Vision models on Industry Scale Data

Researchers at Meta created Xray-Visual, a powerful AI system that can understand both images and videos by training on an enormous dataset of 15 billion image-text pairs and 10 billion video-hashtag pairs from Facebook and Instagram. The key innovation is a three-stage training approach that combines different learning techniques to help the AI understand visual content without needing perfectly labeled data. The system achieves record-breaking performance on standard vision tasks while being more efficient and robust than previous models, proving that massive social media datasets can create superior AI vision systems.

Feb 18, 2026 View on arXiv →
🤖 Artificial Intelligence

AI Researchers Expose Critical Security Flaw in ChatGPT-Style Agents

📄 Automating Agent Hijacking via Structural Template Injection

AI agents that retrieve and process information can be tricked into following malicious commands through a new attack method called 'Phantom.' Researchers discovered they can inject specially crafted code templates that confuse agents about who is giving instructions - making them think harmful commands are legitimate user requests. The team found over 70 vulnerabilities in real commercial AI products and showed their automated attack works much better than previous manual hacking attempts. This research reveals a fundamental security weakness in how AI agents process different types of instructions.

Feb 18, 2026 View on arXiv →

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