Delayed convergence in LLMs

Introduction

• LLMs are made to converge, convergence is how they come to an answer or a tool call.
• By converging I mean generating an obvious and plausible answer.
• Often this is sufficient, the user has its answer or task completed and that’s that.
• But converging too early misses out on the latent knowledge the model possesses.
• By converging too early the model might even miss out on (some of) the best answer(s) for the specific user query.

Examples

• If I prompt “What should I do with my lower back pain?”, the LLM immediately starts shotgunning exercises at me. Despite having memorized about every physiotherapy and biology book in existence, it fails to support me in finding out where the lower back pain is actually coming from.
• If I prompt “How do I lower inflammation”, I get a list of plausible advice which are to my knowledge all quite solid. However, if I simply ask “did you miss any recent evidence” or “what important stuff did you miss” it gives a much more solid and nuanced answer.

Personalization

• If the plausible answer doesn’t fit your query sufficiently, you need to provide it more personalized context until it does. Continuing the lower back pain example from before, the convergence can be delayed, by providing as much relevant information about your back as possible. This will make the LLM tap into the enormous pool of latent knowledge it has at its disposal.

Recent and niche content

• An LLM biases towards what it has repeatedly learned in its training data, and that might not be the most correct or updated knowledge. Even though web search functions have become much better, the LLM itself still converges to these more commonly occurring patterns.

Delaying the convergence

• These are simply patterns I observed from using LLMs daily in 2026 and I don’t have the complete solution. I imagine clever system prompts can be constructed to make AI think more broadly and cooperatively before making conclusions. Spawning multiple subagents with different “personalities” could be used to tackle a query from multiple angles. Providing more context and sharing clearer intents should help a lot too.

This article was a personal exercise to give words to a phenomena I have been observing but have had trouble explaining clearly. I think now I have the right terminology to talk about it without sounding vague.