LLM
Large language models struggle to use information placed in the middle of long contexts, favoring content at the beginning and end. This 'lost in the middle' effect has major implications for RAG systems and AI reliability.
adversarial attacks
A new theoretical framework unifies adversarial vulnerability in neural networks with LLM hallucination, proposing that both arise from a fundamental uncertainty trade-off in learned representations.
fake news detection
New research proposes LLM-MRD, a framework that distills multi-view reasoning from large language models into smaller, efficient detectors for fake news identification.
mechanistic interpretability
Researchers are racing to understand what happens inside neural networks. Mechanistic interpretability could reshape how we build, audit, and trust AI systems — from deepfake detectors to video generators.
AI Video Generation
AI tools for video generation, voice synthesis, and procedural content creation were everywhere at the 2026 Game Developers Conference, signaling a major shift in how games are built.
Deepfake Detection
Reality Defender showcases its enterprise deepfake detection platform at RSAC 2026, targeting growing corporate demand for AI-generated content identification across video, audio, and images.
AI Content Labeling
X is rolling out new features to help users identify and handle AI-generated content on the platform, signaling a broader industry push toward synthetic media transparency and digital authenticity.
AI Regulation
A White House AI policy blueprint argues federal rules should override conflicting state AI laws, a consequential shift for deepfake regulation, disclosure standards, and digital authenticity vendors.
AI Safety
A new paper introduces multi-trait subspace steering to manipulate several behavioral dimensions in AI systems at once, offering a technical lens on alignment failure, misuse, and synthetic media safety.
LLM Bias
A new paper shows that changing only names in prompts can flip LLM verdicts, revealing systematic bias through intervention consistency tests. The findings matter for AI moderation, authenticity review, and automated decision systems.
LLM research
A new paper introduces WASD, a method for finding neurons that are sufficient to explain and steer LLM behavior. The work adds technical insight into controllable generation and interpretable model editing.
LLM Reliability
A new arXiv paper explores how to score the trustworthiness of structured LLM outputs in real time, aiming to make data extraction systems more auditable, calibrated, and safer to deploy.