LLM
New research introduces STED and Consistency Scoring, a systematic framework for measuring how reliably large language models produce structured outputs—critical for production AI systems.
neural-networks
New Stagewise Pairwise Mixing method replaces dense linear layers with O(n log n) complexity, potentially revolutionizing how large AI models are trained.
LLM Inference
New research introduces Yggdrasil, a tree-based speculative decoding architecture that bridges dynamic speculation with static runtime for faster LLM inference.
multimodal AI
The human brain seamlessly integrates sight, sound, and touch. Replicating this took a decade of AI research and seven critical innovations that now power today's video and image generation systems.
JEPA
Meta's Chief AI Scientist argues current generative models are fundamentally flawed. His Joint Embedding Predictive Architecture offers an alternative that could reshape how AI understands video and reality.
AI Agents
Learn how to design production-grade agentic AI systems using LangGraph with two-phase commit protocols, human-in-the-loop interrupts, and safe rollback mechanisms for reliable automation.
AI Industry
2025 marked AI's transition from revolutionary promises to measurable product reality. Here's how the industry matured and what it means for synthetic media.
GUI Agents
Alibaba Tongyi Lab releases MAI-UI, a family of GUI agents achieving state-of-the-art results on AndroidWorld benchmarks, surpassing Gemini 2.5 Pro and other leading models.
LLM Agents
New research uses multi-agent LLM systems simulating venture capitalists to evaluate startups, achieving notable predictive accuracy through collective roleplay-based reasoning.
LLM research
New research explores whether deliberation improves LLM-based forecasting, examining how AI agents can leverage collective reasoning to make better predictions through structured discussion.
AI safety
New research proposes integrating actions, compositional structure, and episodic memory from neuroscience to build safer, more interpretable AI systems that could transform how we approach AI trustworthiness.
AI safety
Researchers introduce DarkPatterns-LLM, a multi-layer benchmark designed to identify and evaluate manipulative behaviors in large language models, advancing AI safety and authenticity research.