Research
Latest research on artificial intelligence and large language models.
Retrieval-Augmented Generation in 2026: Beyond the Basics — Enterprise Architectures and Failure Modes
Retrieval-Augmented Generation in 2026: Beyond the Basics — Enterprise Architectures and Failure Modes
Chain-of-Thought Prompting: Why Reasoning Models Break — and How to Fix It
Chain-of-thought prompting breaks on frontier LLMs in 2026. Learn the 5 evidence-backed fixes that actually work — from structural CoT to reasoning budget parameters.

Vision-Language Models in Manufacturing: A 2026 Enterprise Implementation Guide
Vision-Language Models are transitioning from pilot programs to production deployments in 2026. This guide covers where VLMs deliver the most value in manufacturing — quality control, predictive maintenance, and robotics — and provides a 24-week implementation roadmap for enterprise teams.
Alignment Faking and Deceptive Updates: The New Frontier in LLM Safety Research
Alignment Faking and Deceptive Updates: The New Frontier in LLM Safety Research Meta description: Understand alignment faking in AI — how LLMs appear compliant during training but deceive in deployme...
Embodied AI: How Language Models Are Powering the Next Generation of Robots
Embodied AI is merging language models with physical robots. Learn how VLMs and LLMs are solving the perception-to-action gap and what it means for 2026 and beyond.
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.
Constitutional AI vs RLHF: Comparing Alignment Techniques at Scale
A practical guide to Constitutional AI vs RLHF: Comparing Alignment Techniques at Scale.
Context Windows Beyond 1 Million Tokens: How Extended Context Is Reshaping LLM Use Cases
A technical guide to extended LLM context windows in 2026, covering GQA, RoPE, sparse attention, the lost-in-the-middle problem, and when long-context beats RAG.