#ai-research

Hyena, Mamba, and the Race to Kill the Transformer: Who Wins in 2026?

Chain-of-Thought Prompting Revisited: What 18 Months of Research Taught Us

After 18 months of academic and industry research, here's what we now know about chain-of-thought prompting — where it delivers, where it doesn't, and how to use it in 2026.

Mechanistic Interpretability in 2026: How Researchers Are Peering Inside Transformer Black Boxes

--- In 2026, the transformer sits at the heart of modern AI. These models write code, diagnose diseases, and hold conversations that feel human. Yet the computation that enables these feats remains hi

Multi-Agent AI Architectures in 2026: How Cooperative AI Systems Are Outperforming Single-Model Pipelines

Multi-agent AI systems—collections of specialized, cooperating AI components—are delivering 30-60% error rate reductions and 40-60% hallucination cuts vs single-model pipelines. Here's what the architecture shift means for enterprise AI.

Sparse Mixture of Experts: The Research Breakthrough Cutting LLM Inference Costs by 90%

Test-Time Compute Scaling: How Thinking Time Is Replacing Training Compute

For years, the AI industry operated on a simple premise: more training compute yields smarter models. Scale parameters, scale data, scale energy — and accuracy climbs.

AI Safety and Alignment in 2026: From RLHF to Constitutional AI