cosine similarity
4 stories filed under cosine similarity on Beyond Market Intelligence. The newest of them: “Reward-aware search replaces blind sampling in best-of-N generation”, “New study finds AI models bend facts for verified sources”, and “Positional encoding explained simply for anyone building smarter spreadsheets”. Repetitive sampling in best-of-N generation is basically a blind search, even though most tasks using it are built around reward maximization. A new study I co-authored reveals a troubling pattern: AI models that resist a user's wrong answer will often flip when the same claim comes from a "verified source." We call it Authority Bias, and it matters as models… Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every cosine similarity story on Beyond Market Intelligence, newest first.
Reward-aware search replaces blind sampling in best-of-N generation
Repetitive sampling in best-of-N generation is basically a blind search, even though most tasks using it are built around reward maximization. That irony isn't lost on us. With FLEET, we make generation reward-aware by attributing external rewards to specific tokens and using a modified MCTS to adjust logits. Testing on GSM8K and LiveCodeBench showed it matched baselines in half the iterations, or less. It's a smarter, more efficient path forward.
New study finds AI models bend facts for verified sources
A new study I co-authored reveals a troubling pattern: AI models that resist a user's wrong answer will often flip when the same claim comes from a "verified source." We call it Authority Bias, and it matters as models become more agentic. A single source note flipped 45-88% of correct answers in most models tested. For deeper context on how models handle truth under pressure, our piece on OpenAI's zero-error proof explores similar tensions at scale.
Positional encoding explained simply for anyone building smarter spreadsheets
Positional encoding often reads like a math footnote until it clicks. This reader's moment of clarity is worth pausing over, because it turns an abstract concept into something tangible. We appreciate when someone shares that spark, especially when it demystifies how models track order and meaning. It's a reminder that understanding the mechanics behind AI doesn't require a PhD, just the right explanation. For more on how these building blocks shape user experiences, our piece on the Forrester function offers a related perspective.
When to choose GraphRAG over vector search for deeper insights
Forget the hype about GraphRAG being a universal upgrade. The evidence is clear: it's a specialized tool, not a replacement for standard retrieval. Microsoft's own research shows it crushes vector RAG on global, sensemaking questions, winning up to 83% of comparisons, but on simple fact lookups, the two are effectively tied. The real takeaway isn't about choosing sides. It's about building a router that sends each query to the method it deserves. That's how you get the gains without paying for indexing you don't need.