« Felipe Antolinez »

LLMs had a rough start to 2026.

Last week, our AI agent told a user their Monday meeting was on Sunday. The reason? In 2025, January 12th was a Sunday—and that's what the model learned during training.

We're seeing this pattern across many date-related tasks: models confidently inferring the wrong day of the week, or hallucinating years that weren't in the input text.

The obvious fix is to inject today's date into every prompt. However, that's not always what you want. For example, if you're extracting dates from text, adding context about "today" can bias the output and cause hallucinations of its own.

Is this the new Y2K bug we'll have to deal with every January? Or will future models be more robust to year transitions?

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