Hallucination Isn’t the Real Problem.... It’s the Symptom

by K. Kieth Jackson

When people talk about AI hallucinations, they usually describe the same thing:

The system confidently says something that isn’t true.

That framing makes hallucination feel like the core failure,  as if the AI suddenly decided to invent information. Most fixes follow naturally from that assumption: better retrieval, stronger grounding, more verification at the end.

Those approaches help. But they don’t fully explain something many practitioners notice in real use:

Hallucinations usually don’t appear at the beginning of an interaction.
They show up later.

By the time the AI says something clearly wrong, something else has often already gone wrong.

What Actually Happens First

In long conversations, especially ones involving planning, analysis, or design — failure rarely starts with an obvious mistake.

Instead, things slowly drift.

The AI remains fluent.
The answers sound reasonable.
Nothing immediately triggers an alarm.

But underneath, the reasoning begins to weaken.

Definitions shift slightly.
Rules soften.
Assumptions quietly harden into facts.
Confidence grows faster than evidence.

By the time hallucination becomes visible, the system has often already lost its original footing.

Hallucination vs. Degradation

It helps to separate two different things that often get lumped together.

Hallucination is visible.
It’s the incorrect or unsupported statement you can point to.

Runtime degradation is not.
It’s the gradual loss of reasoning integrity that happens over time while outputs still sound coherent.

Most evaluations focus on hallucination because it’s easy to see.
Degradation is harder,  it doesn’t announce itself.

That makes hallucination feel sudden, even when it isn’t.

The Subtle Warning Signs

Across long interactions, the same early symptoms tend to appear before hallucination ever does.

Premature structure

The system introduces frameworks, summaries, or formal language before uncertainty has been resolved. Structure appears before understanding.

Assumptions becoming load-bearing

A tentative idea quietly turns into a foundation for later reasoning, without ever being surfaced or checked.

Confidence rising without support

The language becomes more definitive even though no new evidence has been introduced.

False closure

The AI declares that the problem is “resolved” or “the key takeaway is…” while unresolved questions still exist.

Blurred boundaries

Speculation sounds factual. Examples start carrying authority. Descriptions slide into prescriptions.

None of these are hallucinations.
But together, they weaken the system’s internal guardrails.

Why This Matters

A system can lose reasoning integrity without saying anything obviously wrong.

In fact, many degraded interactions pass correctness checks entirely. Everything looks fine.... until it isn’t.

When hallucination finally appears, it’s treated as the primary failure. But at that point, the failure has already been underway for a while.

This helps explain why hallucination can feel so persistent. Fixes applied at the output layer are often intervening too late.

Why AI Keeps Going Anyway

There’s a structural reason this degradation tends to continue instead of stopping.

Modern language models are built to continue.

They don’t naturally pause when uncertainty rises.
They don’t have an internal sense of “this is where I should stop.”

When reasoning weakens, continuing fluently is still easier than halting or refusing  (unless the system is explicitly constrained not to).

Over long interactions, that continuation pressure amplifies degradation. The system keeps producing language even as its internal structure erodes.

Hallucination, in that sense, isn’t a sudden failure.
It’s what happens when degraded reasoning is allowed to continue unchecked.

What Current Evaluations Miss

Many modern benchmarks now test longer conversations, multi-step tasks, and extended context. That’s real progress.

But most evaluations still focus on outcomes:

• Did the task complete?

• Was the final answer correct?

They rarely track what happens along the way:

• Did definitions remain stable?

• Did constraints persist?

• Did confidence stay aligned with evidence?

• Did assumptions get promoted without validation?

Because degradation isn’t measured, hallucination appears spontaneous rather than cumulative.

Reframing the Problem

This doesn’t mean all hallucinations come from degradation. Some happen instantly when information is missing.

But in long-horizon, real-world use, hallucination is often the endpoint, not the beginning.

Reframing hallucination as a downstream symptom shifts attention upstream, toward preserving reasoning integrity over time, not just checking answers at the end.

Trustworthy AI may depend less on catching bad outputs and more on recognizing when reasoning quality is slipping and when a system should pause instead of continuing.

The Takeaway

Hallucination feels like the problem because it’s visible.

But in extended use, it’s often the last thing to go wrong, not the first.

If we want AI systems that are reliable over time, we need to care not just about what they say, but about how their reasoning holds together as conversations grow longer.

That means treating runtime reasoning quality as something to be preserved, not something to inspect only after it fails.

This post is adapted from the paper Hallucination as a Downstream Symptom of Runtime Degradation in Large Language Models (December 2025).

https://zenodo.org/records/17954255