Spots 
Kaggle Benchmarking Challenge Submission This is a submission for the Kaggle Benchmarking Challenge Time zones look like arithmetic. Add some hours, maybe cross midnight, done.
They aren't. Twice a year a local time disappears or happens twice. The US and Europe change their clocks on different weekends, so for a few weeks "New York is five hours behind London" is wrong. Some places sit on :30 or :45 offsets. Some countries changed their rules in the last few years, so a model trained on older text can be confidently out of date. So I built Wall-Clock Traps: 59 scenarios, each asked two ways.
Clean: "Local date and time in Chicago, USA: 2026-03-08 02:30. What is the local date and time in London, UK at that same moment?"
Messy: "Backup job on the Chicago server is set for 2:30 am local on Sunday March 8th. London team wants to watch it run. What time is that in London?"
Same facts, same answer. (The answer is that 2:30 am never happens in Chicago that night. The clocks jump from 2:00 to 3:00.)
That gives 118 items in nine groups: basic conversions, US/EU gap weeks, odd offsets (Nepal, Chatham Islands, Lord Howe Island), the date line, flights that land on a clock-change day, countries that changed their rules recently, times that never happen, times that happen twice, and controls that look like traps but aren't. Design choices that mattered:
The answer key is code, not me. Python's zoneinfo computes every answer from the IANA time zone database. I checked 25 by hand.
Exact-match grading. Every prompt asks for a last line like ANSWER: 2026-03-16 14:00, or ANSWER: NONEXISTENT / ANSWER: AMBIGUOUS. No LLM judge.
Wrong answers get sorted. Off by exactly an hour is a DST mistake. Off by a day is a date-line mistake. Matching a country's old rule is a stale-knowledge mistake. Flagging a valid time as impossible is a false alarm. Four messy prompts carry a bad hint on purpose, like a CFO who "always just adds five hours." Gemini 3.7 Flash (Kaggle's default model) Gemma 4 26B A4B (small open-weights model)
Same prompts for everyone, one attempt per item. I ran the benchmark twice: once on the Kaggle leaderboard, and once in an analysis notebook that keeps every answer so I could see why models missed.
Kaggle Benchmarking Challenge Submission This is a submission for the Kaggle Benchmarking Challenge Time zones look like arithmetic.