The case for zero-error horizons in trustworthy LLMs

arxiv.org

April 2, 2026

2 min read

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47/100

Summary

Zero-Error Horizon (ZEH) is proposed as a metric for evaluating the maximum range of error-free performance in large language models (LLMs). An evaluation of GPT-5.2's ZEH reveals significant insights into its limitations, including its inability to accurately count to five.

Key Takeaways

The concept of Zero-Error Horizon (ZEH) is proposed as a measure for the maximum range that a language model can solve without errors.
Evaluation of GPT-5.2's ZEH revealed that it cannot compute simple tasks, such as determining the parity of a string or balancing parentheses.
ZEH correlates with accuracy in language models but reveals differing behaviors and insights into the emergence of algorithmic capabilities.
Computing ZEH incurs significant computational costs, but methods exist to achieve up to a tenfold speedup using tree structures and online softmax.

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Historical count: 5 · 5 saved in sample · Historical sample

Analysis ready

Community Sentiment

Mixed

Positives

The article emphasizes the importance of quantifying reliability for specific tasks, which can help users better understand LLM limitations and avoid overgeneralization.
There is a recognition that LLMs excel at generating scripts and automation, suggesting their strength lies in assisting with complex tasks rather than executing them flawlessly.

Concerns

Many users mistakenly believe LLMs possess human-like reasoning and learning capabilities, leading to unrealistic expectations about their performance in tasks like accounting and coding.
The concept of zero-error horizons may not be applicable to LLMs, as these models operate more like Kahneman's System 1, which limits their reliability in complex evaluations.