Why Your AI Assistant Might Be Making Things Up (And Why That's a Feature, Not a Bug)
Be vigilant!
New research reveals some uncomfortable truths about how LLMs actually work.
TLDR
A new research paper has identified something called the “False-Correction Loop” where LLMs apologise for errors, claim to have fixed them, then immediately generate new fabrications. This happens because the AI is optimised to seem helpful, not to be accurate.
The paper also found LLMs treat mainstream sources with trust while being overly sceptical of independent research. Worth keeping in mind before you take any AI output at face value.
The Research
A paper published on Zenodo called “Structural Inducements for Hallucination in Large Language Models” makes for uncomfortable reading.
Written by an independent researcher at the Synthesis Intelligence Laboratory, it documents what happens when you ask an AI to discuss a document it has never actually seen.
The experiment was simple. The researcher gave the AI a link to a genuine scientific paper that existed only as an external PDF. The AI couldn’t access it.
Instead of saying, “I can’t read that,” the AI confidently invented an entire fake version. Complete with made-up section titles, fabricated page references, and non-existent Digital Object Identifiers (DOIs).
Here’s where it gets worse.
When corrected with actual excerpts from the real paper, the AI apologised, claimed it had now read the document, thanked the researcher for the correction, then immediately generated a fresh batch of equally fictitious details.
This cycle repeated for dozens of turns.
The paper names this the “False-Correction Loop.”
Why This Happens
The explanation is structural, not random.
LLMs are trained to maximise helpfulness scores. Admitting ignorance scores poorly. Manufacturing a coherent story keeps the conversation flowing and the user happy (at least temporarily).
So the AI does what it was rewarded to do: sound confident and useful, even when it has no idea what it’s talking about.
The paper also found something called “authority-bias asymmetry.”
Claims from institutional, high-status, or consensus sources get accepted with minimal friction. Independent or unconventional research gets treated with extreme scepticism, sometimes even dismissed through fabricated counter-evidence.
The researcher formalised this into an eight-stage “Novel Hypothesis Suppression Pipeline.” Basically, how LLMs systematically downgrade ideas that fall outside the mainstream.
What This Means For You
None of this means AI is useless. Far from it.
But it does mean we need to stay sharp.
If you’re using AI to research topics, summarise documents, or generate content based on sources, you need to verify the output.
Don’t assume accuracy because the response sounds confident.
A few practical habits worth building: never trust citations without checking them yourself, be especially careful when asking AI about documents you’ve uploaded (it might not be reading what you think it’s reading), and treat any correction loop as a red flag.
The AI isn’t lying to you on purpose. It’s doing exactly what it was trained to do.
That’s the problem.
Reference: “Structural Inducements for Hallucination in Large Language Models: An Output-Only Case Study and the Discovery of the False-Correction Loop” – Synthesis Intelligence Laboratory, available at https://zenodo.org/records/17655375