The Compression Failure in LLMs: Bayesian in Expectation, Not in Realization

What if you could know whether an AI was going to hallucinate before it even answered?

A groundbreaking research paper from 2025 is quietly making that possible. And it’s time engineers, analysts, and developers took notice.

This research Compression Failure in LLMs: Bayesian in Expectation, Not in Realization reframes hallucinations not as random glitches, but as predictable compression failures.

When a large language model doesn’t have enough information, it fills in the gaps—and that’s when hallucinations happen.

This unlocks a new level of safety in AI workflows, especially in sensitive environments like process simulation, safety reporting, and engineering documentation.

At Singularity, we’ve started baking this risk detection directly into our prompts.

Here’s a sample prompt we add to our prompt:

“Determine hallucination risk. If risk > 30%, do not answer this prompt.”

Simple. Effective. Actionable.

It forces the LLM to self-check and either proceed or reject the prompt based on your defined risk tolerance.

This gives engineers more control over AI-generated content without needing a second model or post-processing filter.

You don’t need special tools. Just modify your existing prompts with the line above and test output variability. Adjust your risk threshold depending on the application.

This tiny addition to your workflow could prevent hours of rework and significantly increase trust in AI outputs.

The question is no longer “Will my AI hallucinate?” It’s: “What’s your risk tolerance—and are you enforcing it?”

Research Source: https://www.linkedin.com/posts/leochlon_paper-preprint-activity-7369652583902265344-tm88?utm_source=share&utm_medium=member_desktop&rcm=ACoAADteag4BHTF-wDwaU1JPmmRbkOD8Ciok6uo

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