Where the Goblins Come From
Unexpected AI behavior isn’t random - it emerges during generation. A look at why patterns spread and why control must happen in real time.
OpenAI recently published a technical explanation (OpenAI (April 29, 2026). Where the Goblins Came From) of a behavior they observed across model generations - an unexpected pattern that emerged from a single reinforced signal and spread beyond its original context.
Their post is admirable in its candor. It also surfaces something uncomfortable about modern AI systems.
They don't just make mistakes. They produce behavior that is unexpected, inconsistent, or difficult to explain. Outputs can drift. Reasoning can shift mid-response. The system may appear confident in one moment and uncertain in the next.
These behaviors are often described as anomalies. They're not.
What We're Seeing
Across different models and different labs, similar patterns continue to appear.
Systems can produce correct answers but fail to express them. They can follow rules in one context and not in another. They can generate responses that are coherent but not stable.
Sometimes this shows up as hallucination. Sometimes as inconsistency. Sometimes as behavior that feels off without being obviously wrong.
The specifics vary. The pattern does not.
Why It Feels Surprising
Much of the current framing assumes that intelligence is the core problem.
If a system behaves unexpectedly, the instinct is to ask: does it have enough data, was it trained correctly, is the model capable enough.
But these questions assume that behavior is a direct reflection of knowledge. In practice, it isn't.
A Different Lens
Behavior is not static.
As a response is being formed, internal signals evolve. Competing interpretations interact. Confidence changes. Direction shifts.
The system is not retrieving a fixed answer. It is constructing one over time.
When that process is not governed, small deviations can compound. Early assumptions can persist. Confidence can drift away from underlying support.
What emerges at the end of a response is not just what the system knows. It's how it behaved while producing it.
The Source of the Goblins
From this perspective, unexpected behavior is not mysterious. It is the natural result of behavior that is not consistently governed during generation.
The system is capable. The knowledge is often present. But the process that determines what gets expressed is not stable.
OpenAI's own framing points in the same direction. Reinforcement does not guarantee that learned behaviors stay neatly scoped to the condition that produced them. Once a pattern is strengthened, it can spread in ways that are difficult to predict in advance.
In their case, mitigation involved constraining the surface output. That can reduce visible effects, but it operates at a different layer than the one where the behavior is formed.
Why This Matters
As systems become more capable, this gap becomes more visible - not less.
Greater capability introduces more possible interpretations, more competing signals, and more sensitivity to context. Without a corresponding increase in control, behavior becomes harder to predict.
What looks like an edge case is often a symptom of something more general.
A Simple Conclusion
This is not an intelligence problem. It is a behavior problem during generation.
Unexpected outputs, inconsistencies, and goblins are different expressions of the same underlying issue. How behavior unfolds as a response is being formed.
If behavior is dynamic, then it has to be addressed dynamically. Not only before a response is generated. Not only after it is produced. But at the point where it is being formed.
We agree. So we did something about it.
This perspective is informed by ongoing work at XyloIQ on how AI behavior can be stabilized and governed as responses are formed.
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Reference: OpenAI (2026). Where the Goblins Came From.
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