I asked Claude about a library released in March 2025 (after its January cutoff). Instead of saying smth like "I don't know, that's after my cutoff," it fabricated a detailed technical explanation - architecture, API design, use cases. Completely made up, but internally consistent and plausible.

What's confusing: the model clearly "knows" its cutoff date when asked directly, and can express uncertainty in other contexts. Yet it chooses to hallucinate instead of admitting ignorance.

Is this a fundamental architecture limitation, or just a training objective problem? Generating a coherent fake explanation seems more expensive than "I don't have that information."

Why haven't labs prioritized fixing this? Adding web search mostly solves it, which suggests it's not architecturally impossible to know when to defer.

Has anyone seen research or experiments that improve this behavior? Curious if this is a known hard problem or more about deployment priorities.