Temporal Reasoning

Dashboard numbers are seductive because they look obedient. Revenue goes up, traffic dips, latency spikes, inventory turns over, temperature drifts, volatility clusters. Put the sequence into a chart and the pattern seems almost polite. Then someone asks an LLM what happened. The model answers fluently. It may even sound like an analyst who has seen too many quarterly review decks and has developed a protective layer of confidence. But fluency is not temporal understanding. A model can describe a curve, name a trend, and still fail to understand which segment comes next, whether a transformation is correct, or whether a discontinuity is an error or a legitimate feature of the process. ...

Calendars are harmless until a computer has to reason about them. A human can say, “Ram has a dentist appointment in one hour, must pick up his insurance card from home, needs cash from the ATM, and travel takes 15, 20, 30, or 40 minutes depending on the route.” We see a small planning problem. A logic system sees actions, states, deadlines, durations, inertia, and a very annoying question: should every possible minute become a Boolean object? ...