In this episode of ChatGPT and Beyond with Fexingo, Lucas and Luna dig into a surprising limitation of today's large language models: they still can't reliably answer simple calendar or date-arithmetic questions. Using a concrete example from June 2026—asking a model what day of the week June 5 falls on—they explore why temporal reasoning breaks down for transformer-based architectures, even as these models ace the bar exam and write production code. They discuss the difference between memorized date patterns and genuine temporal reasoning, the role of tokenization in numerical errors, and why model makers from OpenAI to Anthropic are pouring research into 'grounding' problems. The hosts also touch on how this limitation matters for AI agents scheduling meetings or handling logistics, and whether future models might need built-in calendar APIs rather than pure reasoning. A focused, 10-minute look at one brittle seam in the AI stack—and why it's harder to fix than you'd think.

#AI #LargeLanguageModels #GenerativeAI #TemporalReasoning #CalendarProblems #OpenAI #Anthropic #AIlimitations #Tokenization #MachineLearning #Technology #Business #FexingoBusiness #BusinessPodcast #ChatGPT #AIResearch #Productivity #AIagents

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