Date and Time Reasoning Bugs

“Schedule it for the Friday after next” is one of the most dangerous strings you can hand a language model, because it will confidently return a date, that date will be well formatted, and there is roughly no chance anyone downstream will check it. The model has no clock Start with the thing that is easy to forget: a language model is a pure function of its context. It has no system clock, no timezone database access at inference time and no notion of when “now” is. If the current date is not in the context, the model does what it does with any missing variable — it infers a plausible one from the distribution, which means from the density of dates in its training data. So a model asked for “next Tuesday” with no anchor is computing an offset from a guess, and the guess skews towards its training cutoff. Worse, models will often state the assumed date confidently, or not state it at all, which removes the one signal a reviewer could have used. This is a hallucination in the strict sense: a specific claim about the world, produced with no information behind it. The four failures 1. Missing anchor Everything above. The fix is one line in the system prompt and it is astonishing how often it is missing. Include the full instant, not just the date: Current time: 2026-08-03T14:05:00+02:00 (Europe/Amsterdam, Monday) . Giving the weekday explicitly removes a computation, and giving the offset and the IANA zone removes two more. 2. Date arithmetic, which is just arithmetic Counting days across month boundaries, adding 90 days, computing an age at a past date, finding the number of business days in a range. Every weakness on the numerical reasoning page applies, plus irregular bases: months of unequal length, leap years, and the leap-year rule’s century exceptions. Off-by-one errors here are systematic rather than random, which is what makes them survive casual review. 3. Timezones, offsets and DST The richest source of silent bugs. An offset is not a timezone — +01:00 is a f